Alternate methodology for Electricity demand assessment ...€¦ · Alternate methodology for...

Alternate Methodology for Electricity Demand Assessment and Forecasting

World Energy Council India

Alternate methodology for Electricity demand assessment and forecasting 2

Table of Contents

Abbreviations ..................................................................................................................................................10

1. Executive Summary....................................................................................................................................11

1.1. Objective and scope of the study ........................................................................................................12

2. Review of forecasting methodologies of major sectors in India ...........................................................13

2.1. Coal .....................................................................................................................................................13

2.1.1. Introduction ................................................................................................................................13

2.1.2. Demand projection of Coal ........................................................................................................14

2.1.3. Summary ...................................................................................................................................21

2.2. Renewable Energy ..............................................................................................................................22

2.2.1. Introduction ................................................................................................................................22

2.2.2. Renewable Energy Potential .....................................................................................................22

2.2.3. Projections for renewables ........................................................................................................24

2.2.4. RE Actual Implementation vs. Targets ......................................................................................25

2.2.5. Targets beyond 2022 for Renewables ......................................................................................26

2.2.6. Summary ...................................................................................................................................26

2.3. Oil & Natural Gas ................................................................................................................................27

2.3.1. Introduction ................................................................................................................................27

2.3.2. Oil and Gas Potential .................................................................................................................28

2.3.3. Oil & Gas Projections ................................................................................................................28

2.3.4. Summary ...................................................................................................................................31

2.4. Power ..................................................................................................................................................32

2.4.1. Introduction ................................................................................................................................32

2.4.2. Review of the existing methodologies .......................................................................................32

2.4.3. Demand forecasting ..................................................................................................................41

2.5. Summary .............................................................................................................................................49

3. Linkage between primary energy and power demand ............................................................................50

3.1. India: Current energy scenario ............................................................................................................50

3.2. Historical linkage between Primary Energy and Power demand ........................................................50

3.3. Effect of New influencers on the Power sector ...................................................................................52

3.4. Effect of New Influencers on the Primary Energy ...............................................................................54

3.5. Expected linkage between Primary energy and power demand.........................................................55

4. Gaps in existing demand assessment and forecasting methodologies ...............................................57

4.1. General gaps identified based on review of commonly employed methodologies in India ................58

4.2. Gaps in forecast due to impact of economic factors ...........................................................................61

4.2.1. Overall economic scenario during the period when 18th EPS was prepared ............................62

4.2.2. Reasons for slow demand growth in FY 2013-14 .....................................................................63

4.3. Specific gaps on methodologies employed in forecasts by CEA ........................................................64

4.3.1. Draft National Electricity Plan, 2016 ..........................................................................................64

4.3.2. 19th Electric Power Survey, 2017 ..............................................................................................67

4.4. IESS 2047 – analysis of the gap between demand and supply projections .......................................69

5. Proposed alternate approach and methodology .....................................................................................70

5.1. Proposed hybrid method .....................................................................................................................70

5.2. Use of bottom up approach .................................................................................................................70

5.2.1. Proposed 18-step alternate methodology .................................................................................71

6. Approach adopted for selected state .......................................................................................................81

6.1. Rajasthan - Profile ...............................................................................................................................81

6.2. Power sector in the state.....................................................................................................................82

6.3. Use of a bottom up approach ..............................................................................................................84


6.3.1. Forecast at Circle level ..............................................................................................................84

6.3.2. Consolidation of Circle level data ..............................................................................................84

7. Baseline correction of historical data ......................................................................................................87

8. Implementation of alternate methodology ...............................................................................................97

8.1. Analysis of historical data – category wise to gather insights and understand behavior ....................97

8.2. Selection of forecasting methods for each forecast period .................................................................99

8.3. Use of trend method ............................................................................................................................99

8.4. Use of Holts-Winter’s multiplicative and additive exponential smoothing .........................................102

8.5. Identification of Independent variables .............................................................................................104

8.6. Development of econometric equations and forecast .......................................................................104

8.7. Impact of Electric Vehicles ................................................................................................................107

8.8. Assessment of specific demand using end use method ...................................................................108

8.8.1. Railways ..................................................................................................................................108

8.8.2. Domestic: Power For All (PFA) and Saubhagya scheme .......................................................108

8.8.3. Agriculture category .................................................................................................................109

8.8.4. Open Access and Captive .......................................................................................................109

8.9. Assessment of additional demand ....................................................................................................110

8.9.1. Housing: Chief Minister’s Jan Awas Yojana / Rajasthan housing board ................................110

8.9.2. Review of Master Plans ...........................................................................................................111

8.9.3. Jaipur Metro Rail project ..........................................................................................................112

8.9.4. Smart City ................................................................................................................................112

8.9.5. Delhi Mumbai Industrial Corridor .............................................................................................113

8.9.6. Rajasthan State Industrial Development and Industrial Corporation (RIICO) .........................114

8.10. Assessment of latent demand .........................................................................................................115

8.11. Impact of Energy Efficiency.............................................................................................................117

8.12. Development of forecast scenarios .................................................................................................118

8.13. Estimation of the future loss trajectory ............................................................................................120

8.14. Estimation of load factor and peak demand....................................................................................121

8.15. Finalization of forecast results ........................................................................................................125

8.16. Validation of results with traditional methodologies ........................................................................127

9. Summary of demand forecast results ....................................................................................................129

9.1. Rajasthan – Forecast summary ........................................................................................................130

9.1.1. Forecast of Electricity consumption .........................................................................................130

9.2. Forecast at DISCOM level ................................................................................................................136

9.2.1. JVVNL – Forecast of electricity consumption ..........................................................................136

9.2.2. AVVNL – Forecast of electricity consumption .........................................................................139

9.2.3. JdVVNL – Forecast of electricity consumption ........................................................................142

10. Functional strategies for supply side to meet the projected demand ..............................................145

10.1. Existing supply position in Rajasthan ..............................................................................................145

10.2. Options for Supply side evaluation .................................................................................................146

10.3. Assessment of supply capacity required to meet projected demand for the state .........................146

10.3.1. Electricity requirement of state consumers to be met by sources other than Distribution utilities

...........................................................................................................................................................147

10.3.2. Electricity requirement to be met by Distribution utilities in the state ....................................148

10.3.3. Projection of generation sources to meet projected electricity demand ................................149

10.3.4. Projection of generation source to meet projected peak demand .........................................153

10.4. Projected supply position in Rajasthan ...........................................................................................158

10.5. Functional strategies for managing higher integration of renewables ............................................159

10.5.1. Managing the load curve .......................................................................................................159

10.5.2. Safe limit of RE integration without storage ..........................................................................162

10.5.3. Impact of storage in integration .............................................................................................163

10.5.4. Need for capacity building .....................................................................................................165


11. Data challenges ......................................................................................................................................166

12. Recommendations..................................................................................................................................167

13. Annexure .................................................................................................................................................169

13.1. Forecast results – Circle wise .........................................................................................................169

13.1.1. Alwar ......................................................................................................................................169

13.1.2. Bharatpur & Dholpur ..............................................................................................................172

13.1.3. Dausa and Karauli .................................................................................................................175

13.1.4. Jaipur city circle .....................................................................................................................178

13.1.5. JPDC .....................................................................................................................................181

13.1.6. Jhalawar and Baran ...............................................................................................................184

13.1.7. Kota and Bundi ......................................................................................................................187

13.1.8. Sawai Madhopur and Tonk ....................................................................................................190

13.1.9. Ajmer .....................................................................................................................................193

13.1.10. Bhilwara ...............................................................................................................................196

13.1.11. Nagaur .................................................................................................................................199

13.1.12. Udaipur ................................................................................................................................202

13.1.13. Rajsamand...........................................................................................................................205

13.1.14. Chittorgarh and Pratapgarh .................................................................................................208

13.1.15. Banswara and Dungarour ....................................................................................................211

13.1.16. Jhunjhunu ............................................................................................................................214

13.1.17. Sikar .....................................................................................................................................217

13.1.18. Jodhpur City circle ...............................................................................................................220

13.1.19. Jodhpur district circle ...........................................................................................................223

13.1.20. Jalore ...................................................................................................................................226

13.1.21. Pali and Sirohi .....................................................................................................................229

13.1.22. Barmer and Jaisalmer .........................................................................................................232

13.1.23. Bikaner .................................................................................................................................235

13.1.24. Sri Ganganagar ...................................................................................................................238

13.1.25. Hanumangarh ......................................................................................................................241

13.1.26. Churu ...................................................................................................................................244

13.2. Sample data formats .......................................................................................................................247


List of Figures Figure 1: Estimate reserves of coal as on March 2017. Total 308.8 BT ..............................................................13 Figure 2: Coal requirement based on demand from power stations and industries .............................................18 Figure 3: Total supply side projections .................................................................................................................21 Figure 4: Share of installed capacity ....................................................................................................................22 Figure 5: Installed capacity - Wind .......................................................................................................................22 Figure 6: Installed capacity - Solar .......................................................................................................................22 Figure 7: State wise estimated solar potential (GW) – NISE Estimate ................................................................23 Figure 8: State wise estimated wind power potential (GW) – NIWE estimate at 80m level .................................23 Figure 9: State wise estimated biomass potential (MW) ......................................................................................23 Figure 10: State wise estimated SHP potential (MW) ..........................................................................................24 Figure 11: Renewable Energy targets for 2022 (GW) ..........................................................................................24 Figure 12: Renewable Energy: Target vs Actual (MW) ........................................................................................25 Figure 13: Total projected installed capacity by FY 27. Total 640 GW ................................................................26 Figure 14: Consumption of petroleum products ...................................................................................................27 Figure 15: Domestic production and import of oil .................................................................................................27 Figure 16: Gas production and import ..................................................................................................................27 Figure 17: Snapshot a working tab in SPSS software ..........................................................................................35 Figure 18: Consumption of Energy and Share of Electricity in Primary energy consumption ..............................51 Figure 19: Primary demand by fuel ......................................................................................................................51 Figure 20: New influencers expected to impact electricity demand- supply .........................................................52 Figure 21: Periodicity and application of demand forecasting ..............................................................................57 Figure 22: Variation between the projected demand in 18th EPS and actual demand ........................................58 Figure 23: Comparison between bottom up and top down approaches ...............................................................71 Figure 24: 18 Step alternate demand forecasting methodology ..........................................................................71 Figure 25: Representation of a straight line method ............................................................................................72 Figure 26: Case study: Time series method .........................................................................................................74 Figure 27: Case study: use of econometric forecasting technique ......................................................................75 Figure 28: Illustration of using additional loads in forecast ...................................................................................77 Figure 29: Illustration of forecast results ...............................................................................................................78 Figure 30: Case study: Illustration of a loss trajectory ..........................................................................................79 Figure 31: Rajasthan - GSDP ...............................................................................................................................81 Figure 32: Sectoral contribution to GVA ...............................................................................................................81 Figure 33: Electricity requirement and availability scenario in Rajasthan ............................................................83 Figure 34: Share of electricity demand across categories in the State ................................................................83 Figure 35: Circle wise feeder data accessed from utility ......................................................................................88 Figure 36: Calculation of average monthly supply hours .....................................................................................89 Figure 37: Illustrative - circle wise average supply gap ........................................................................................89 Figure 38: Category wise monthly served and unserved demand .......................................................................92 Figure 39: Category wise yearly served and unserved data ................................................................................92 Figure 40: Historical data post baseline correction (Illustration)...........................................................................92 Figure 41: JPDC – Consumers: Category wise composition ...............................................................................97 Figure 42: JPDC - Consumers: Growth over previous year .................................................................................97 Figure 43: JPDC Consumers: CAGR growth for 9 periods ..................................................................................97 Figure 44: JPDC – Historical sales: Category wise composition..........................................................................98 Figure 45: JPDC - Historical sales: Growth over previous year ...........................................................................98 Figure 46: JPDC - Historical sales: CAGR growth rates ......................................................................................98 Figure 47: Decomposition of historical data by Holt-Winter's method ................................................................102 Figure 48: Modeling of historical data using Holt-Winter's method ....................................................................102 Figure 49: Average consumption per domestic consumers in Rajasthan (in kWh/Consumer/Year) .................116 Figure 50: Comparison of trends of consumption in Rajasthan up to FY 32 ......................................................116 Figure 51: Illustration of served - unserved demand for the state ......................................................................119 Figure 52: % Actual T&D loss for the state.........................................................................................................120 Figure 53: Rajasthan: Decomposition of load factor trend (POSOCO report) ...................................................122 Figure 54: Estimated load factor for the state ....................................................................................................123 Figure 55: Historical peak demand for the state of Rajasthan ...........................................................................124 Figure 56: Share of supply sources as of FY17 .................................................................................................145 Figure 57: Contracted capacity of Rajasthan as of February FY17 (MW) .........................................................145 Figure 58: Trend of PLF for coal based thermal power stations ........................................................................151


Figure 59: Scenario 1: Projected supply position (MW) and change in share of generation sources................158 Figure 60: Scenario 1: Projected supply position (MUs) and electricity to be supplied by generation sources .158 Figure 61: Typical load curve in a day - Rajasthan ............................................................................................159 Figure 62: Average generation profile of wind and solar in a day ......................................................................160


List of Tables Table 1: Likely capacity addition of RES ..............................................................................................................14 Table 2: Estimated Coal requirement in the year 2021-22 and 2026-27 .............................................................15 Table 3: Coal requirement by Coal Power Stations (MTce) .................................................................................16 Table 4: Coal requirement by Industries (MTce) ..................................................................................................17 Table 5: Coal production projections (MT) ...........................................................................................................20 Table 6: Projections for Coal Imports as per the scenarios (MT) .........................................................................20 Table 7: RPO Trajectory defined by the Government ..........................................................................................25 Table 8: Demand projections from FY17-22 based on CAGR .............................................................................28 Table 9: Segment wise demand projections ........................................................................................................29 Table 10: Energy mix as per current policy scenario ...........................................................................................31 Table 11: Share of Oil and Gas ............................................................................................................................31 Table 12: Sample equations for univariate econometric model ...........................................................................36 Table 13: Sample equations for multivariate econometric model.........................................................................37 Table 14: Projections at All-India level for the period FY 2016-17 to FY 2020-21 ...............................................44 Table 15: Projections at All-India level for the period FY 2022-23 to FY 2026-27 ...............................................45 Table 16: CAGR of the projections .......................................................................................................................45 Table 17: Perspective electricity demand for FY 2031-32 and 2036-37 ..............................................................45 Table 18: Comparison of Energy Requirement between 18th and 19th EPS ......................................................46 Table 19: Comparison of Peak demand between 18th EPS and 19th EPS .........................................................46 Table 20: Energy requirement and peak demand an all-India basis without DSM, EE measures.......................47 Table 21: Energy requirement and peak demand an all-India basis with DSM, EE measures ............................48 Table 22: Regression equations developed .........................................................................................................48 Table 23: Forecasts developed using both the methodologies ............................................................................48 Table 24: Energy Intensity ....................................................................................................................................50 Table 25: Share of electricity in primary demand .................................................................................................55 Table 26: Share of primary energy under two scenarios in 2022 and 2040 .........................................................55 Table 27: Comparison of Energy Requirement between 18th EPS Projections and actuals ................................61 Table 28: Tentative list of independent variables .................................................................................................74 Table 29: Present structure of power sector utilities ............................................................................................82 Table 30: Consolidation of Circles for forecast .....................................................................................................84 Table 31: Monthly average supply gap of a sample circle ...................................................................................89 Table 32: Illustrative: Extrapolation of gap in supply hours highlighted for a sample circle .................................90 Table 33: Illustrative: Calculation of supply hours for historical data highlighted for a sample circle...................90 Table 34: JVVNL: Baseline corrected historical data ...........................................................................................93 Table 35: AVVNL: Baseline corrected historical data ...........................................................................................94 Table 36: JdVVNL: Baseline corrected historical data .........................................................................................95 Table 37: JPDC (Sample Circle): Baseline corrected historical data ...................................................................96 Table 38: Selection of forecasting methods .........................................................................................................99 Table 39: Details of category wise approach to forecasting .................................................................................99 Table 40: Base forecast (Unrestricted) developed for JPDC circle using Trend method ...................................101 Table 41: Base forecast (Unrestricted) developed for JPDC circle using Holt-Winter's method .......................103 Table 42: List of independent variables considered for econometric method ....................................................104 Table 43: Econometric equations developed for the Sample circle JPDC .........................................................105 Table 44: Base forecast (Unrestricted) developed for JPDC circle using Econometric method ........................106 Table 45: Additional electricity requirement (MUs) due to EVs in Rajasthan .....................................................108 Table 46: Additional electricity requirement (MUs) from Railway traction ..........................................................108 Table 47: Electricity requirement (MUs) from OA and Captive ..........................................................................110 Table 48: Data for State housing scheme as on Oct’17 .....................................................................................111 Table 49: Major towns in Rajasthan as per population ......................................................................................111 Table 50: Demand projected in the DMIC investment regions in Rajasthan ......................................................113 Table 51: Anticipated energy requirement due to additional demand from new projects at State level (MUs) .114 Table 52: Estimated latent demand due to behavioral aspect for Rajasthan (in MUs) ......................................117 Table 53: Electricity Savings potential across categories ..................................................................................117 Table 54: % savings estimated due to energy efficiency ...................................................................................118 Table 55: Projected loss trajectory for the state .................................................................................................120 Table 56: Calculation of energy requirement for the state .................................................................................121 Table 57: Rajasthan: Load factor data ...............................................................................................................122 Table 58: Annual Peak demand for state: Using forecasting results using Trend method ................................123


Table 59: Unrestricted peak demand of Rajasthan ............................................................................................123 Table 60: Monthly peak demand for the state determined using forecasting methods ......................................124 Table 61: FY 17: Actual vs Forecast for 3 methods ...........................................................................................126 Table 62: Comparison of forecast results with 19th EPS results for the State (MUs) ........................................127 Table 63: Comparison of forecasts for year FY32 (MUs) ...................................................................................128 Table 64: Summary of the electricity requirement at state level.........................................................................135 Table 65: Summary of peak demand at state level ............................................................................................135 Table 66: Demand projections for the State (in MUs) considered for supply side evaluation ............................146 Table 67: Electricity demand from Open Access, Captive and Railways ...........................................................147 Table 68: Expected consumer demand to be met by rooftop solar ....................................................................148 Table 69: Net addressable electricity demand at consumer end to be met by Distribution utilities ...................149 Table 70: Net addressable electricity demand at state boundary to be procured by Distribution utilities ..........149 Table 71: Expected capacities to be retired by 2022 and 2027 .........................................................................151 Table 72: Summary of projections - generation sources ....................................................................................152 Table 73: Balance electricity demand to be met by coal based sources ...........................................................153 Table 74: Coal capacities required to meet net addressable electricity demand ...............................................153 Table 75: Peaking availability of the generating units ........................................................................................154 Table 76: PLFs of generation sources considered to meet peak demand .........................................................155 Table 77: Quantum of peak demand met by various generation sources other than coal .................................155 Table 78: Ramp up/ down requirement of Rajasthan as of FY22 ......................................................................162 Table 79: Announced, Contracted and Under Construction Storage Capacity by Technology Type ................164 Table 80: Alwar - Forecast of electricity consumption using trend method ........................................................169 Table 81: Alwar - Forecast of electricity consumption using HW method ..........................................................170 Table 82: Alwar - Forecast of electricity consumption using econometric method ............................................171 Table 83: Bharatpur and Dholpur - Forecast of electricity consumption using trend method ............................172 Table 84: Bharatpur and Dholpur - Forecast of electricity consumption using HW method ..............................173 Table 85: Bharatpur and Karauli - Forecast of electricity consumption using econometric method ..................174 Table 86: Dausa and Karuali - Forecast of electricity consumption using trend method ...................................175 Table 87: Dausa and Karauli - Forecast of electricity consumption using HW method .....................................176 Table 88: Dausa and Karauli - Forecast of electricity consumption using econometric method ........................177 Table 89: JCC - Forecast of electricity consumption using trend method ..........................................................178 Table 90: JCC - Forecast of electricity consumption using HW method ............................................................179 Table 91: JCC - Forecast of electricity consumption using econometric method ..............................................180 Table 92: JPDC - Forecast of electricity consumption using trend method .......................................................181 Table 93: JPDC - Forecast of electricity consumption using HW method ..........................................................182 Table 94: JPDC - Forecast of electricity consumption using econometric method ............................................183 Table 95: Jhalawar and Baran - Forecast of electricity consumption using trend method .................................184 Table 96: Jhalawqr and Baran - Forecast of electricity consumption using HW method ...................................185 Table 97: Jhalawar and Baran - Forecast of electricity consumption using econometric method .....................186 Table 98: Kota and Bundi - Forecast of electricity consumption using trend method ........................................187 Table 99: Kota and Bundi - Forecast of electricity consumption using HW method ..........................................188 Table 100: Kota and Bundi - Forecast of electricity consumption using econometric method ...........................189 Table 101: Sawai Madhopur and Tonk - Forecast of electricity consumption using trend method....................190 Table 102: Sawai Madhopur and Tonk - Forecast of electricity consumption using HW method ......................191 Table 103: Sawai Madhopur and Tonk - Forecast of electricity consumption using econometric method ........192 Table 104: Ajmer - Forecast of electricity consumption using trend method .....................................................193 Table 105: Ajmer - Forecast of electricity consumption using HW method ........................................................194 Table 106: Ajmer - Forecast of electricity consumption using econometric method ..........................................195 Table 107: Bhilwara - Forecast of electricity consumption using trend method .................................................196 Table 108: Bhilwara - Forecast of electricity consumption using HW method ...................................................197 Table 109: Bhilwara - Forecast of electricity consumption using econometric method .....................................198 Table 110: Nagaur - Forecast of electricity consumption using trend method ...................................................199 Table 111: Nagaur - Forecast of electricity consumption using HW method .....................................................200 Table 112: Nagaur - Forecast of electricity consumption using econometric method .......................................201 Table 113: Udaipur - Forecast of electricity consumption using trend method ..................................................202 Table 114: Udaipur - Forecast of electricity consumption using HW method ....................................................203 Table 115: Udaipur - Forecast of electricity consumption using econometric method .......................................204 Table 116: Rajsamand - Forecast of electricity consumption using trend method.............................................205 Table 117: Rajsamand - Forecast of electricity consumption using HW method ...............................................206 Table 118: Rajsamand - Forecast of electricity consumption using econometric method .................................207


Table 119: Chitorgarh and Pratapgarh - Forecast of electricity consumption using trend method ....................208 Table 120: Chitorgarh and Pratapgarh - Forecast of electricity consumption using HW method ......................209 Table 121: Chitorgarh and Pratapgarh - Forecast of electricity consumption using econometric method ........210 Table 122: Banswara and Dungarpur - Forecast of electricity consumption using trend method ......................211 Table 123: Banswara and Dungarpur - Forecast of electricity consumption using HW method ........................212 Table 124: Banswara and Dungarpur - Forecast of electricity consumption using econometric method ..........213 Table 125: Jhunjhunu - Forecast of electricity consumption using trend method ..............................................214 Table 126: Jhunjhunu - Forecast of electricity consumption using HW method ................................................215 Table 127: Jhunjhunu - Forecast of electricity consumption using econometric method ...................................216 Table 128: Sikar - Forecast of electricity consumption using trend method .......................................................217 Table 129: Table 92: Sikar - Forecast of electricity consumption using HW method .........................................218 Table 130: Sikar - Forecast of electricity consumption using econometric method ...........................................219 Table 131: JCC - Forecast of electricity consumption using trend method ........................................................220 Table 132: JCC - Forecast of electricity consumption using HW method ..........................................................221 Table 133: JCC - Forecast of electricity consumption using econometric method ............................................222 Table 134: Jodhpur DC - Forecast of electricity consumption using trend method ...........................................223 Table 135: Jodhpur DC - Forecast of electricity consumption using HW method ..............................................224 Table 136: Jodhpur DC - Forecast of electricity consumption using econometric method ................................225 Table 137: Jalore - Forecast of electricity consumption using trend method .....................................................226 Table 138: Jalore - Forecast of electricity consumption using HW method .......................................................227 Table 139: Jalore - Forecast of electricity consumption using econometric method..........................................228 Table 140: Pali and Sirohi - Forecast of electricity consumption using trend method .......................................229 Table 141: Pali and Sirohi - Forecast of electricity consumption using HW method ..........................................230 Table 142: Pali and Sirohi - Forecast of electricity consumption using econometric method ............................231 Table 143: Barmer and Jaisalmer - Forecast of electricity consumption using trend method ...........................232 Table 144: Barmer and Jaisalmer - Forecast of electricity consumption using HW method ..............................233 Table 145: Barmer and Jaisalmer - Forecast of electricity consumption using econometric method ................234 Table 146: Bikaner - Forecast of electricity consumption using trend method ...................................................235 Table 147: Bikaner - Forecast of electricity consumption using HW method .....................................................236 Table 148: Bikaner - Forecast of electricity consumption using econometric method .......................................237 Table 149: Sri Ganganagar - Forecast of electricity consumption using trend method .....................................238 Table 150: Sri Ganganagar - Forecast of electricity consumption using HW method........................................239 Table 151: Sri Ganganagar - Forecast of electricity consumption using econometric method ..........................240 Table 152: Hanumangarh - Forecast of electricity consumption using trend method ........................................241 Table 153: Hanumangarh - Forecast of electricity consumption using HW method ..........................................242 Table 154: Hanumangarh - Forecast of electricity consumption using econometric method ............................243 Table 155: Churu - Forecast of electricity consumption using trend method .....................................................244 Table 156: Churu - Forecast of electricity consumption using HW method .......................................................245 Table 157: Churu - Forecast of electricity consumption using econometric method ........................................246


Abbreviations

Abbreviation Full form

BNEF Bloomberg New Energy Finance

CAGR Compounded Annual Growth Rate

CEA Central Electricity Authority

DISCOM Distribution company/utility

DSM Demand side management

EE Energy Efficiency

EV Electric Vehicle

EVSE Electric Vehicle Supply equipment

FAME Faster Adoption and Manufacturing of (Hybrid) and Electric Vehicles

GDP Gross Domestic Product

GSDP Gross State Domestic Product

GoI Government of India

GW Gigawatt

HW Holt-Winters

kWh Kilo Watt hour

IESS India energy security scenarios

IRENA International Renewable Energy Agency

MoP Ministry of Power

MNRE Ministry of New and Renewable Energy

MTce Metric Tons of coal equivalent

MU Million Units

MW Mega Watt

NEP National Energy Policy

NITI National Institution for Transforming India

PEUM Partial end use methodology

PFA Power For All

PSU Public Sector Undertaking

RE Renewable energy

RPO Renewable energy obligation

SEC Specific energy consumption

WEC World Energy Council


1. Executive Summary

Over the last half a decade, the Indian power sector has witnessed success stories and has undergone dynamic

changes. However, the road ahead is entailed with innumerable challenges that result from the gaps between

what is planned and what the sector has been able to deliver. Demand forecasting of power, thus, has an

important role to play in effective planning and in minimizing the gaps.

The subject of forecasting has been in existence for

decades. It involves prediction of future power demand over

different planning horizon. It is an essential tool for planning

of generation capacities and commensurate transmission

and distribution systems, which will be required to meet the

future electricity requirement. Reliable planning of capacity

addition for future is largely dependent on accurate

assessment of future electricity demand. Electricity demand

forecasting is an essential exercise for every utility as it forms

the basis for the development and optimization of power

portfolio across various term time horizons.

The methods adopted for electricity demand forecasting

have also evolved over time. Previously, extrapolation of

past trends used to be the primary method. However, with

the growing impact of macro and micro economic factors,

emergence of alternative technologies (in supply and end-

use), demographic and lifestyle changes etc., it has become

imperative to use modeling techniques which capture the effect of factors such as price, income, population,

technology and other economic, demographic, policy and technological variables. The future will demand the use

of more hybridized and probabilistic approaches to forecast the electricity requirement more accurately.

In India, the Electric Power Survey (EPS) carried out by Central Electricity Authority (CEA) is the primary

forecasting study based on which all-planning activities in the power sector are carried out. CEA undertakes the

study periodically based on historical data using established methodologies. The forecast results are developed

for distribution utilities, state, region and at a national level for short, medium and long-term horizons. One of the

observed gap in the results of 18th EPS, released in December 2011, has been the YoY variation between

forecast and actual electricity requirement. For the period from FY 2011-12 to FY 2015-16, the actual energy

demand was lower by up to 11.39% and the peak demand was lower by upto 16.60%. On average, the demand

has been lower by 5% on YoY basis. This has left the country with supply overhang with a large newly added

capacity distressed with no PPAs.

CEA in 19th EPS, released in January 2017, has also highlighted the difference and scaled down the energy

forecasts for 19th EPS by around 14.35% in FY 2016-17, 17.79% for FY 2020-21 and by 24.45% for the year FY

2026-27. The variation between what is projected and actuals may be dependent on various factors like

methodology adopted, forecasting technique used, data reliability, usage of growth and other input factors etc.

Besides use of appropriate methodology and tool, accuracy of demand forecast will also depend on choosing the

correct baseline data, which takes into account the unserved demand and the latent demand. Therefore, it is

important to look at alternate methodologies that can minimize such variations.

During the draft stage of the National Electricity Plan (NEP), the World Energy Council India (WEC India) had

provided several suggestions. One of the major suggestions was to undertake baseline correction of historical

data before undertaking a forecast. In addition to that, a need was also felt to review the existing demand

Need of electricity demand

forecasting

The draft amendments to Tariff Policy

released in May 2018 mandates that the

Commission should direct Distribution

licensees to undertake demand

forecasting every year and submit short,

medium and long-term power

procurement plans.

The forecasts will help drive better

decisions on investment, construction

and conservation. It will also play a role

in the process of regulation, tariff setting

and lead to optimized use of resources.


assessment methodologies in order to identify gaps and to develop an alternate methodology. The present study

aims to propose an alternate methodology for electricity demand assessment and forecasting.

1.1. Objective and scope of the study

Objective of the study:

1. Review and identify gaps in the existing electricity demand forecasting methodologies;

2. Develop an alternate bottom up methodology for undertaking electricity demand forecast;

3. Undertake baseline correction of historical data and forecast for a selected state;

4. Validation of forecast results by comparing with results from existing methodologies;

5. Suggest strategies and implementation plan on supply side to meet the forecasted demand.

The following chapters cover in details the scope and each of the objectives.


2. Review of forecasting methodologies of major sectors in India

2.1. Coal

2.1.1. Introduction

India is the third largest coal producer in the world after China and the USA. The total coal production in India

was around 612 million tons (MT) in FY 2015, which increased to 626 MT in FY 2016. Ninety per cent (90%) of

the domestic production comes from public sector coal producers with only ten percent (10%) being produced by

the private sector. India imported a total of 212 MT of coal in FY 2015 and 193 MT in FY 2016, which is equivalent

to ~ 30% of the domestic coal requirement in the country based on tonnage.

The total coal demand in the country was expected to be around 1.2–1.5 billion tons (BT) by 2020 as per various

estimates by the government and independent agencies. Considering this, the Ministry of Coal (MoC) had earlier

set up a target of more than doubling the coal production in the country to reach a production level of 1.5 BT by

FY 2020. The government aimed to increase coal production of Coal India Limited (CIL) to a level of 1 BT by FY

2020, while the balance production was to be met by the private, state and central sector PSUs. However, due

to weak demand from thermal power plants and with a net surplus power situation in the country, the Government,

in the current FY 2017-18, reduced the production target of CIL from 660 MT to 600 MT. With the policy push for

more renewables, the projections in the shorter term are expected to be affected.

Indian coal sector faces a mismatch between the location

of coal reserves and mines. While the reserves are

concentrated in eastern and central India, the high-

demand centers are in the northwest, west and south.

Only around 14% of installed capacity is located in the

eastern region where coal production is concentrated.

The states with high coal availability which account for

95% of coal reserves are

Jharkhand

Odisha

Chhattisgarh

West Bengal,

Madhya Pradesh

Telangana

Since the high-demand centers are not in close vicinity

of coal reserves, a tons of coal has to travel on average

more than 500 km before it is converted to electricity, straining the country’s rail network and leading to transit

losses. Also, for a number of thermal power stations in India, coal transportation requires last mile connectivity

by road, thereby increasing the overall costs.

26.29%

24.58%18.15%

10.21%

8.71%

6.93%5.13%

Jharkhand Odisha

Chhattisgarh West Bengal

Madhya Pradesh Telangana

Others

Figure 1: Estimate reserves of coal as on March 2017. Total 308.8 BT


2.1.2. Demand projection of Coal

Coal contributes over half of India’s primary commercial energy. Though the share of renewable energy is

gradually expected to increase in the coming years, coal is likely to remain as India’s most important source of

energy mainly due to country’s large thermal power capacity and coal’s negligible price volatility.

Methodology and demand projection of coal done by various bodies is presented below:

2.1.2.1. Draft National Electricity Plan 2016: CEA

The projections provided in the draft National Electricity Plan (NEP), released in December 2016, are based on

the draft 19th EPS. The methodology and projections given here are as per the draft NEP 2016. Upon finalization,

the methodology and projections might be modified in the future.

2.1.2.1.1. Methodology

CEA carried out the assessment based on the power requirement of States/UTs considering the past growth

rates and the increase in electrical energy requirement on account of Power for All, Make in India initiatives,

reduction in demand on account of DSM and various efficiency improvement measures being under taken by the

Government. The total electricity requirement on All India basis was worked out accordingly.

For the year 2016-17, coal based generation programme of 921 BU was estimated in consultation with the power

utilities (estimated growth of 6.9%). The total coal requirement of 600 MT for the power plants has been estimated

considering normal monsoon year during the year 2016-17.

With the provisional demand projections and likely Renewable Energy Sources (RES) capacity addition, the coal

based generation has been estimated and accordingly provisional coal requirement has been worked out.

The assumptions considered are given as follows:

The likely capacity addition of RES has been considered in three cases viz.,

Table 1: Likely capacity addition of RES

Case I Case II Case III

115,326 MW 90,326 MW 65,326 MW by the terminal year

2021-22

With the above estimate, the total capacity of RES will be 175 GW in Case-I, 150 GW in Case-II and 125

GW in Case-III respectively by 2021-22

Coal requirement for the year 2021-22 have been worked out considering 30% reduction in Hydro

generation due to failure of monsoon and being supplemented by coal based generation

During 2026-27, the total capacity of RES has been estimated to be 275 GW capacity, considering 175

GW of total capacity at the end of the year 2021-22 and 100 GW capacity addition of RES during

FY2022-27.

Adequate coal is expected to be available for the coal based power plants during 2021-22 and 2026-27

2.1.2.1.2. Projections

Given the above methodology and assumptions, the coal requirement in the year 2021-22 and 2026-27 have

been estimated by CEA as follows:


Table 2: Estimated Coal requirement in the year 2021-22 and 2026-271

Particulars 2021-2022 2026-27

Renewable Scenarios RES: 175 GW RES: 150 GW RES: 125 GW RES: 275 GW

Total coal based generation (BU) 1074.4 1127.4 1177.5 1331.5

Total coal Requirement (MT) 727 763 797 901

Imports by plants designed on imported

coal (MT) 50 50 50 50

Domestic Coal Requirement (MT) 677 713 747 851

With CIL production target of one BT by 2019-20, it is expected that there would be no shortage in the availability

of coal for the power plants during 2021-22 and 2026-27. In addition, coal production from the captive coal blocks

allotted to power utilities would also supplement the availability of domestic coal.

The estimated coal requirement developed by CEA as provided above is more of a top down approach. The

aggregation of coal demand by considering individual sectors in which coal is being used is not available for the

present projections. Historically, from the period FY06 to FY16, 64-68% of the total raw coal is used by power

utilities and 6-10% of the total raw coal by captive power producers. Assuming similar scenario continues for the

next decade also, coal will be required to meet the major power requirements of the country.

2.1.2.2. NITI Aayog

NITI Aayog’s model consists of two projections viz.

Projections from Demand side: Coal demand based on input demand from Power plants and Industries

Projections from Supply side: Coal demand to be met by domestic production and imports

2.1.2.2.1. Demand side Projections

The assumption considered is that coal demand is based on aggregate of (i) input coal demand from coal power

stations and (ii) Industries.

(i) Methodology for input based demand projections

The general assumptions while deriving various scenarios/levels are as follows:

The capacities mentioned include utility and captive power plants fired by coal or lignite, though a lower

PLF is assumed for as utilization will be less

Captive power plants do not use efficient technologies as they are mostly small sized while newer

technologies (e.g. supercritical technology) need larger capacities

The energy generation and coal requirement computed for different capacity addition trajectories also

depend on efficiencies of coal-fired thermal power plants

NITI Aayog’s model has four scenarios:

Level 1: Least effort scenario

Coal based power generation is discouraged due to increasing fuel prices, import dependence, pressure

to reduce carbon emissions, reducing prices of renewable energy etc.

1 Draft National Electricity Plan December 2016


PLF of power plants remains 73% up to 2032 and improves to 74% in 2047

Level 2: Determined effort scenario

Installed capacity will grow rapidly to 297 GW in 2027, and then grow slowly to 333 GW in 2047 due to

increasing coal prices, increasing import dependence and increasing pressures to reduce emissions.

PLF is assumed to improve to 75% for next two decades and to 76% by 2047

Level 3: Aggressive effort scenario

Level 3 assumes a coal-fired capacity addition slightly lower than 8% GDP growth scenario. The growth

rate of capacity addition is assumed to reduce subsequently.

Current PLF will improve to 77% for next two decades and to 78% in 2047

Level 4: Heroic effort scenario

It assumes a coal-fired capacity addition slightly lower than 9% GDP growth scenario.

The growth rate of capacity addition is assumed to reduce subsequently.

Installed capacity will grow to 591 GW in the next 35 years i.e. by 2047 due to improved domestic coal

supply, softening of imported coal prices and availability of more carbon space to countries like India.

Current PLF will improve to 79% for next two decades and to 80% in 2047

Projections on Coal requirement from Power Stations

As per the methodology adopted and defined scenarios, the demand projection of coal from coal based power

station based on different levels is shown in the table below

Table 3: Coal requirement by Coal Power Stations (MTce)

Scenario 2012 2017 2022 2027 2032 2037 2042 2047

Level 1 464 640 776 849 888 883 855 822

Level 2 464 668 863 989 1032 1067 1070 1059

Level 3 464 687 911 1091 1239 1373 1440 1446

Level 4 464 744 1039 1304 1522 1699 1796 1855

(ii) Methodology for assessing aggregated demand projections from Industries

Seven major energy consuming industry sub-sectors have been analyzed to estimate the energy use in each

sub-sector to understand the aggregated demand of coal from industry under different trajectories.

The general assumption considered is that over the years, the energy intensity of the industry sector has been

reducing as several of the units across sectors have moved to processes that are more efficient. The

improvement in Energy Efficiency (EE) has been considered as a major driver for reduction in the Specific Energy

Consumption (SEC) of industry.

Aluminum Cement Chemicals Fertilizer

Iron and Steel Pulp and Paper Textiles Others


The brief about the four scenarios considered as given in the following


This trajectory assumes that there will be no new major government policies for EE. The autonomous

EE penetration levels are also low

The efficiency of the units undergoes marginal reduction/revision by end of the terminal year (2047)

The efficiency of some sub-sector units improves at 5-15% of the efficiency improvement for the units

that opt for EE

The “Others” undergo reduction in energy intensity by a CAGR of ~4%


This level includes a gradual enhancement of penetration of EE in Industry

Industrial units opting for EE would achieve the best efficiency possible in every sub sector

The units not opting for EE also improve their efficiency, but by a much lesser degree

The “Others” undergo a reduction in intensity of about 4-5% CAGR


Building on Level 2, this scenario further increases the EE penetration

The units not opting for EE increase their efficiency across processes at a rate of 20-30% of the efficiency

improvement by units that opt for EE.

The “Others” undergo an efficiency improvement of about 4-5% CAGR


Indicates the maximum possible improvement that can be achieved in the sector

This level further increases EE penetration

In addition, this level assumes that the units not taking up EE undergo an efficiency increase of between

20-50% of the units that opt for EE

The “Others” improve their intensity by about 5-6% CAGR

Projections on Coal requirement from Industries

As per the methodology adopted and defined scenarios, the projection for coal in order to meet the demand from

industries shown in the table below

Table 4: Coal requirement by Industries (MTce)

Scenario 2012 2017 2022 2027 2032 2037 2042 2047

Level 1 285 368 538 754 1017 1272 1561 1761

Level 2 285 363 520 705 925 1145 1354 1486

Level 3 285 358 507 674 874 1084 1239 1354

Level 4 285 351 481 628 817 996 1085 1158

Total demand projections of Coal

The total coal demand based on demand from power station and industries is summarized in the graph below. The graph captures the variation in the coal demand based on the four scenario, which were defined.


Figure 2: Coal requirement based on demand from power stations and industries

2.1.2.2.2. Supply side Projections

NITI Aayog has forecasted coal production till 2047 considering four different scenarios from the supply side. The

assumptions are based on domestic supply potential and the balance to be met from imports.

Methodology

The assumptions while deriving these scenarios/levels are as follows:

Coal is intended to mean both coal as well as lignite.

The average life of a mine is 30 years.

The current mine ability (ratio of India’s techno-economically extractable reserves to prove reserves) of

Indian mines is 34.6%.

Maximum coal reserves percentage to be mined by underground coal mining is limited to 12.3% of total

proved reserves.

This is based on assumption that coal reserves up to 300 m will be mined by opencast (OC) method, half

of reserves between 300 – 600 m depth would be mined by opencast and underground (UG) method

each, and reserves deeper than 600 m will be mined by underground mining.

Collieries own coal consumption= 0.08% of their coal production.

Remaining coal production will be considered as net coal production.

The following provides a brief about the assumptions considered under the four scenarios:


Level 1 assumes that only the currently operating, on-going and planned coal mining projects by CIL

(437 million tons per annum (MTPA)) and SCCL (41 MTPA) and currently allocated captive blocks (43

billion ton geological reserves) will come online.

Production from current (non-captive) mines will reduce by 17% every 5 years (consistent with mine life

of 30 years) due to closure of mines.

Production from captive blocks will start reducing from 2027 onwards as most of the currently producing

captive blocks are new. Coal reserves and mine ability of all reserves will remain at present values.

2583

2545

2800

749

3013

500

1000

1500

2000

2500

3000

3500

2012 2017 2022 2027 2032 2037 2042 2047

Co

al re

qu

ire

me

nt in

MT

ce

Level 1

Level 2

Level 3

Level 4


In this scenario, coal production gradually increases from 582 MTPA in 2011-12 to peak of 866 MTPA in

2037 and then it will start declining and reach 540 MTPA in 2047.

About 85% of the mineable coal reserves will have been extracted by 2047 in this scenario as no new

reserves are added and there is no improvement in mine ability.

In addition, only OC mining, which is easier and cheaper, will be encouraged whereas the UG mining will

be discouraged. So, the UG % will reduce from current of about 9% to 6.4% in 2047


Level 2 projections are consistent with realistic (business as usual) projected scenario based the 12th

Five Year Plan until 2022.

Given that the production for 2012-13 fell about 18 million tons short of the target of 575 million tons, it

assume that the total shortfall from the target in 2017 would be 50 million tons. This results in an annual

production increase of about 5% per annum up to 2017, and about 4% up to 2022.

Proved coal reserves will grow at a reduced pace of 1% p.a. as most of the prognosticated coal bearing

area (75%) has been explored. There would be some improvement in mine ability due to technological

improvement.

In this scenario, coal production will grow to a peak at 1191 MTPA in 2037 and decline marginally by

2047 to 1157 MTPA.

About 62% of mineable coal reserves would have been extracted by 2047. OC mining will be encouraged

but UG mining will not be paid much attention. Therefore, UG mining’s percentage will increase just

slightly from current 9% to 9.3% in 2047.


Level 3 is consistent with the optimistic scenario projections until 2022 in the 12th Five Year Plan,

tempered by slower-than-expected increase in production. The rate of increase of production reduces

slightly going forward.

Proved coal reserves will grow at about 1.3% p.a. and there would be further improvement in mine ability.

With these positive conditions for coal based energy supply, coal production will be 1400 MTPA in 2047.

About 55% of mineable coal reserves would have been extracted by 2047. UG mining will also be

encouraged progressively to tap deeper coal reserves and its share will increase to 10.7% in 2047.


Level 4 is the most optimistic, assuming full encouragement for coal based energy supply.

Proved coal reserves will grow at 1.5% p.a., production will reach about 1400 MTPA in 2032 as

anticipated in the Integrated Energy Policy document, and mine ability will increase better than in other

levels. In this scenario, coal production will increase to about 1608 MTPA in 2047, almost consistent with

the high-case scenario of global coal production as per Global Coal Production Outlook.

In this scenario, about 48% of mineable coal reserves would have been extracted by 2047. UG mining

will be emphasized significantly along with technological advancements but its share will remain confined

to 12.3% in 2047 due to geological constraints.


Projections

The table below shows the coal production up to the year 2047 as per the four scenarios which were developed

Table 5: Coal production projections (MT)

Scenario 2012 2017 2022 2027 2032 2037 2042 2047

Level 1 578 692 766 799 793 779 672 540

Level 2 578 719 893 1034 1128 1169 1175 1157

Level 3 578 766 1014 1169 1283 1350 1377 1400

Level 4 578 786 1048 1236 1384 1498 1551 1608

Coal Imports

Total import requirements for coal would also depend on how demand from other sectors such as iron and steel

and cement changes over time.

75% of domestically produced coal continues to be used for power generation as currently done, coal import

requirements from the power sector in 2047 range between 260 MTPA (if coal production and coal-based

capacity addition follow a determined path) and 660 MTPA (if coal production and coal-based capacity addition

follow a heroic path) even if a heroic effort scenario of technology adoption requiring least coal plays out.

Table 6: Projections for Coal Imports as per the scenarios (MT)

Scenario 2012 2017 2022 2027 2032 2037 2042 2047

Level 1 124 261 450 828 1417 2094 2943 3716

Level 2 124 204 296 384 666 1117 1544 1938

Level 3 124 165 270 411 561 787 943 1023

Level 4 124 207 400 588 752 927 1058 1118


Supply side total projections

Total supply side demand projection based on summation of coal production and import is presented below:

Figure 3: Total supply side projections

Coal has always been the primary source of energy for the country and as per estimates the share of coal in

India’s primary energy supply was between 45-50% and is expected to have significant share in the years to

come.

Since the major requirement of coal will be from power plants and currently the thermal plants running at a lower

PLF of ~ 57% as on June 2017 at an all India level. With demand for elecrtcity set to increase, the requirements

will have to be met from coal only. Even though no new coal based plants may be approved for the next ten

years, but post 2027, there is likelihood of capacity addition which is likely to translate into a coal demand of 1.1-

1.4 billion tonnes. With the domestic production scenario projected to plateau out for level 2 and level 3 scenarios

post 2030, the additions post 2027 will require some amount of import dependence for the country.

2.1.3. Summary

The coal projections given in the section above are based on methodologies developed by CEA and NITI

Aayog.

The projections developed by CEA in the Draft NEP up to FY 2026-27 are predominantly ‘Top Down’

projections and have been worked out based on an historical growth rate and considering possible

scenarios of renewable capacity addition. The aggregation of the demand from individual demand

sectors has not been projected.

The projections developed by NITI Aayog are from the coal demand and supply side up to the year 2047.

The projections have been developed considering four scenarios.

o For the demand side projections, the total aggregated demand has been worked out by

considering the demand from Power sector and from eight major Industrial sector wherein coal

is required.

o The supply side projections has been worked out by estimating the coal production from the

domestic sector and the balance to be met from imports.

Historically, between 60%-70% of the raw coal is being consumed by the power sector and the balance

is the demand from the Industrial sector. Going ahead, the share is expected to come down Y0Y but will

remain as the major power source in the next two decades.

4256

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24232726

0

500

1000

1500

2000

2500

3000

3500

4000

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2012 2017 2022 2027 2032 2037 2042 2047

MT

ce

Level 1

Level 2

Level 3

Level 4


2.2. Renewable Energy

2.2.1. Introduction

Demand for renewable sources of energy has witnessed

growth in the recent past. Some of the factors, which

supported the penetration of the renewables, are policy

focus, reducing costs of the sector and environmental

considerations.

The following figure highlights the growing share of

renewables in the generation capacity in the country.

In a period of five years, the total installed

renewable capacity of renewables has more

than doubled from 24.5 GW in March 2013 to

about 70.6 GW in July 2018.

While the percentage contribution from thermal

has remained constant, the shift has majorly

been due to decrease in capacity additions from

the Hydro sector.

The renewable energy sector is traditionally led by the

wind power sector followed by solar. However, over the

recent period, the trend has shifted towards solar with

solar capacities leaping from ~5 GW to 17 GW in a

period of 1-2 years due to policy and reform push from

the government and with falling prices of the solar

panels. Wind on the other hand has maintained more of

a steady growth over the years though the installation has

picked up in the period from FY 16 to FY 17.

Conducive policy and regulatory framework at the

Central as well as State Government level is further

aiding in driving demand for renewable energy in the

country. Renewable Purchase Obligations (RPO) have

been mandated upon the Obligated Entities in line with

the aggressive capacity addition targets of the

Government of India.

2.2.2. Renewable Energy Potential

India has an estimated renewable energy potential of about 900 GW from commercially exploitable sources viz.

Wind – 102 GW (at 80 meter mast height); Small Hydro – 20 GW; Bioenergy – 25 GW; and 750 GW solar

power, assuming 3% wasteland is made available. The potential of major renewable energy sources is given in

the following section

2.2.2.1. Solar

As per National Institute of Solar Energy (NISE) total solar energy potential in India is about 748.98 GW with

maximum potential in Rajasthan region 142.31GWp.

66%

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66%

19%13%

2%

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10%

20%

30%

40%

50%

60%

70%

Thermal Renewable Hydro Nuclear

Mar-12 Jun-17

Figure 4: Share of installed capacity

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Figure 5: Installed capacity - Wind

161 461 1205 2319 2632 37445130

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Figure 6: Installed capacity - Solar


Figure 7: State wise estimated solar potential (GW) – NISE Estimate

Rajasthan (25%), Gujarat (21%) and Madhya Pradesh (14%) are leading in solar installed capacities contributing

to 60% of total installed capacity.

The National Tariff Policy 2016 specifies, “SERC shall reserve a minimum % for purchase of solar energy …such

that it reaches 8% of total consumption of energy by March 2022”. Considering the policy directives, the

Government had notified state wise RPO obligations to be fulfilled by the states.

2.2.2.2. Wind

Wind power constitutes ~9.8 percentage of total

power installed capacity and is ~55.8 percentage

of the total Renewable Energy (RE) Capacity

generated in the country as on June 2017. The

National Institute of Wind Energy (NIWE) has

developed the Wind Atlas of India. NIWE also

collects data from Solar Radiation Resource

Assessment stations to assess and quantify solar

radiation availability and develop Solar Atlas of

the country. As per NIWE estimate, wind energy

potential in India is about ~100 GW at 80m level

and ~300 GW at 100m level.

2.2.2.3. Biomass

The current availability of biomass in India is

estimated at about 500 million metric tonnes per

year. The studies carried out by the Ministry of

New and Renewable Energy show a potential of

18000 MW, which includes the biomass

availability at about 120-150 million metric tonnes

per annum covering agricultural and forestry

residues. This apart, about 7000 MW additional

power could be generated through bagasse-

based cogeneration. A total of approximately

500 biomass power and cogeneration projects

aggregating to 4760 MW capacity have been

installed in the country for feeding power to the grid. In addition, around 30 biomass power projects aggregating

to about 350 MW are under various stages of implementation.

38 36 34

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Figure 8: State wise estimated wind power potential (GW) – NIWE estimate at 80m level

Figure 9: State wise estimated biomass potential (MW)


2.2.2.4. Small hydro power

In India, hydro projects up to 25 MW station

capacities have been categorized as Small Hydro

Power (SHP) projects. The estimated potential for

power generation in the country from such plants

is about 20,000 MW. Ministry of New and

Renewable Energy has created a database of

potential sites of small hydro and 6,474 potential

sites with an aggregate capacity of 19,749.44 MW

for projects up to 25 MW capacity have been

identified. The installed capacity as on June 2017

is 4379.86 MW.

2.2.2.5. Waste to Energy

The total estimated potential of Waste to Energy in the country is about 2556 MW of which 1022 MW is estimated

to be industrial wastes. The states with higher potential of waste to energy are Maharashtra (287 MW), Uttar

Pradesh (176 MW), Tamil Nadu (151 MW), West Bengal (148 MW), Delhi (131 MW) and Andhra Pradesh

(123MW). Rest of the potential is spread out across the states in India.

2.2.3. Projections for renewables

The Ministry of New and Renewable Energy (MNRE) is the nodal Ministry for all matters relating to new and

renewable energy. The Ministry has been facilitating the implementation of broad spectrum programs including

harnessing renewable power, renewable energy to rural areas for lighting, cooking and motive power, use of

renewable energy in urban, industrial and commercial applications and development of alternate fuels and

applications.

The Government of India, in 2015, while reviewing the potential of the renewable sector, realized the importance

of the contribution that renewables can make in the country to meet the growing energy needs. Considering the

future demand, the government revised the targets for the renewable energy and set an ambitious target of

achieving 175 GW of renewable energy capacity in the country by year 2022. Though there is no definite

methodology behind the fixation of the target, it was more a target set to utilize the potential, which has to be

backed up by policy, and reforms push from the Government.

The source wise breakup of RE targets is indicated in the following figure:

Wind, 60 GW

SHP, 10 GW

Biomass, 10 GW

Utility Scale Solar Plants,

40 GW

Ultra-Mega Solar Parks, 20 GW

DecentralsedSolar

Rooftop, 40 GW

Solar, 100 GW

Figure 11: Renewable Energy targets for 2022 (GW)

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Figure 10: State wise estimated SHP potential (MW)


The 175 GW target was further noted as part of India’s Intended Nationally Determined Contributions (INDC)

submission to the United Nations Framework Convention on Climate Change (UNFCCC), though not as part of

the official pledge, but as part of the various mitigation strategies.

As per the provisions laid out in the National Tariff Policy, 2016 and in order to achieve the target of 175 GW

renewable capacity by 2022, the MoP in consultation with MNRE has notified the long term growth trajectory of

RPOs for Non-Solar as well as Solar as under:

Table 7: RPO Trajectory defined by the Government

2.2.4. RE Actual Implementation vs. Targets

Of 175 GW, the primary resource is solar and is slated to contribute 100 GW of the total. Most of large MW scale

utility projects are expected to come up in solar parks. These parks will have appropriately developed land with

all clearances, a transmission system, water access, road connectivity, communication network, etc. The Solar

Energy Corporation of India (SECI) is developing these solar parks in collaboration with the respective state

governments. The choice of implementation agency is left to the state governments. It could be done by a) the

designated state PSU, b) a joint venture between SECI and the state PSU, c) SECI, d) private entrepreneurs.

The implementation agency may sell or lease land plots to prospective developers.

Even though the solar sector has picked up in the last three years, there is a long way to go to meet the targets.

Figure 12: Renewable Energy: Target vs Actual (MW)

The government has set year on year targets to be achieved, but there was considerable delays. In addition,

there are concerns on various fronts that also has led to holding back on many fronts. Lenders and investors find

it risky to invest in renewable energy because of the nature of uncertainty in generation. There are concerns

regarding regulatory issues related to land acquisition and government clearances for project. Concerns

regarding purchase of power by state distribution companies, backing down of renewables by distribution

companies, non-payment of dues on time are also few of the concerns which the players are grappling with. The

next few years up to 2022 will be crucial for the sector and will eventually decide the future.

23023

34293

88394493

100000

60000

100005000

0

20000

40000

60000

80000

100000

Solar Wind Biomass SHP

Mar-15 Mar-16 Jul-18 Mar-22 (Target)

Trajectory 2016-17 2017-18 2018-19

Non-solar 8.75% 9.50% 10.25%

Solar 2.75% 4.75% 6.75%

Total 11.50% 14.25% 17.00%


2.2.5. Targets beyond 2022 for Renewables

The draft National Electricity Plan 2016 provides the

projections for Renewable capacity to be added

beyond the year 2022. CEA carried out a simulation

by considering energy scenarios and projected an

addition of 100 GW renewable capacity in the period

from 2022 to 2027 taking the total renewable

installed capacity to 275 GW.

CEA estimates that in a period of next ten years, the

required installed capacity will double from the

current of ~ 330 GW to about ~640 GW by end of

2026-27. The projections were carried out based on

the total demand that has been projected up to

FY 2026-27. Hydro, Gas and Nuclear based

capacity were given the priority due to their inherent

advantages to move towards a Low Carbon Growth.

Renewable capacity was considered as a must run capacity in the system. The balance requirement has been

allotted to coal.

2.2.6. Summary

The projections for renewable energy are more of a top down policy decision and not from a bottom up

approach from the individual sectors. The targets have been set considering the potential that the sector

has in the country.

In the period of last five years, the total installed renewable capacity of renewables has more than

doubled from 24.5 GW in March 2013 to about 58.3 GW in June 2017, however the growth in the

individual sectors have been varied.

With the evolving policy push and growing interest of the stakeholders, the renewable capacity addition

is expected to grow but it is still early to estimate whether the target of 175 GW will be met or not.

The current projections of other sectors like coal etc. have also been developed considering possible

scenarios of renewable capacity additions by the year 2022.

CEA in the draft NEP has developed a possible scenario beyond the year 2022, wherein it is assumed

that by the year 2022, the target of 175 GW will be met and by 2027, the renewable capacity addition will

be 275 GW, which will constitute 43% of the projected capacity addition from the current share of ~17%.

43%

39%

11%

5% 2%

Renewables

Coal

Hydro

Gas

Nuclear

Figure 13: Total projected installed capacity by FY 27. Total 640 GW


2.3. Oil & Natural Gas

2.3.1. Introduction

India is a net importer of Oil & Gas and imports

~ 80% of its Oil requirements. The sector primarily

fuels the important sectors of transport and industries

like fertilizers, power etc.

From global shares of oil and gas of 33% and 24% in

energy consumption in 2015 respectively, the IEA

estimates the respective shares of oil and gas to

converge at 25% each by 2035. In India, the shares of

oil and gas in energy consumption in 2015-16 were

26% and 6.5%, respectively. 2

It is expected that in the medium term while the share

of oil may not come down, share of gas would rise.

India traditionally lagged in the exploration of the oil

and gas sector and was more reliant on imports. With

increasing population and economic growth, the

demand for Oil & Gas has been growing consistently.

However, the same has not been backed up with

sufficient exploration and production.

As per the Draft Energy Policy 2017 released by NITI

Aayog, from 2005-06 to 2015-16, oil production

increased by 15% while consumption of petroleum

products increased by 62% and, gas production

remained static, though there was an upswing in gas

consumption between 2009-12 due to gas supplies

from KG D6 after which the production has been

constantly declining, whereas the consumption of gas

increased by 38%.

Adjoining figure3 highlights the increasing trend of

natural gas imports to meet the growing demand. The

reliance on imports has exposed India to external price

volatility and risks the energy security of the country.

The variation in the global oil prices pose challenges for

the Indian economy and its growth. The concern over crude oil prices stems from India’s energy import bill of

around $150 billion, expected to reach $300 billion by 2030. However, the decline in the oil prices had a positive

impact on the economy. India is targeting to reduce its imports by 10% by 2022 and plans to meet three forth of

its demand domestically by the year 2040.

2 Draft Energy Policy 2017 3 Annual Report 2016-17, MoP&G

148 157 158 166185 195

0

50

100

150

200

250

300

2011-12 2012-13 2013-14 2014-15 2015-16 2016-17

Consumption of Petro-Products (MMT)

172 185 189 189 203 216

38 38 38 37 37 37

0

50

100

150

200

250

300

2011-12 2012-13 2013-14 2014-15 2015-16 2016-17

Import (MMT) Domestic (MMT)

13 13 13 1417

19

48

41

35 34 32 34

0

10

20

30

40

50

2011-12 2012-13 2013-14 2014-15 2015-16 2016-17

LNG Import (MMT) Natural Gas (BCM)

Figure 14: Consumption of petroleum products

Figure 15: Domestic production and import of oil

Figure 16: Gas production and import


2.3.2. Oil and Gas Potential

The estimated reserves4 of crude oil in India as on 31st March 2016 is 621.10 MT against 635.60 MT a year ago.

Geographical distribution of Crude oil indicates that the maximum reserves are in the Western Offshore (39.79%)

followed by Assam (25.89%), whereas the maximum reserves of Natural Gas are in the Eastern Offshore

(36.79%) followed by Western offshore (23.95%). The estimated reserves of Natural Gas in India as on

31st March 2016 stood at 1227.23 Billion Cubic Meters (BCM) as against 1251.90 BCM a year ago.

2.3.3. Oil & Gas Projections

2.3.3.1. Petroleum Planning and Analysis Cell (PPAC)

Petroleum Planning and Analysis Cell (PPAC) is the official source of data and policy analysis on the hydrocarbon

sector in the country. It monitors and analyses trends in prices of crude oil, petroleum products and natural gas

and their impact on the oil companies and consumers, and prepare appropriate technical inputs for policymaking.

It also collects, compiles and disseminates data on the domestic oil and gas sector in a continuous manner and

maintain the data bank, prepares periodic reports on various aspects of oil and gas sector.


The demand for petroleum products has been calculated based on the aggregated Net Demand (Production +

Import – Export - Consumption) of individual products in the country based on the past trends. Based on the

historical data available from V Plan (1974-79) for each of the petroleum products, PPAC computed the

Compounded Annual Growth Rate (CAGR) for each Five-year plan and the Annual CAGR for all the petroleum

products up to XII Plan (2012-17). Based on the past growth rate, the demand of petroleum products and natural

gas for the 13th Plan was worked out.

2.3.3.1.2. Aggregated Projections5

Based on the historical CAGR, demand estimates for the FY 2017-18 to FY 2021-22 is given below. The demand

projections have been arrived at considering the growth rate of individual sectors and the demand of each product

is then aggregated to arrive at the total demand for petroleum products.

Table 8: Demand projections from FY17-22 based on CAGR

Products FY 2017-18 FY 2018-19 FY 2019-20 FY 2020-21 FY 2021-22

1. Petroleum Products ('000 MT)

LPG 22597 23271 23868 24342 24770

MS 24527 26587 28774 31095 33651

NAPHTHA 12516 14185 14923 15388 15388

ATF 9263 10022 10829 11673 12517

SKO 6549 6352 6162 5977 5798

HSDO 86762 92050 97859 104101 110785

LDO 400 400 400 400 400

LUBES 3120 3207 3297 3389 3485

FO/LSHS 7845 7845 7845 7845 7845

BITUMEN 6305 6544 6687 6878 7165

PET COKE 11419 12651 14000 15476 17089

4 Energy Statistics 2017, MoSPI 5 PPAC, MoP&G


OTHERS 6142 6071 6103 6085 6067

Total POL 197445 209185 220747 232649 244960

2. Natural Gas (MMSCMD) 494 523 552 586 606

2.3.3.2. Petroleum and Natural Gas Regulatory Board (PNGRB)

The Petroleum and Natural Gas Regulatory Board (PNGRB)6 was constituted under The Petroleum and Natural

Gas Regulatory Board Act, 2006 to protect the interests of consumers and entities engaged in specified activities

relating to petroleum, petroleum products and natural gas and to promote competitive markets and for matters

connected therewith or incidental thereto. The board has also been mandated to regulate the refining, processing,

storage, transportation, distribution, marketing and sale of petroleum, petroleum products and natural gas

excluding production of crude oil and natural gas so as and to ensure uninterrupted and adequate supply of

petroleum, petroleum products and natural gas in all parts of the country.

PNGRB authorized the industry group to conduct a study Vision 2030: Natural Gas Infrastructure in India, which

was released on May 2013. The current report provides the projections for the Natural gas for the period from FY

2012-13 to FY 2029-30.


Power, fertilizers and other industries consume the bulk of natural gas in India. The basic premise behind the

projections carried out in FY 2012-13 was that, for the power sector, there will be shortage of domestic coal and

prices of imported coal will increase which will shift the demand towards natural gas. For the fertilizer sector, the

assumption was increased food requirement will call for higher fertilizer requirements and natural gas is used in

those industries.

The following provide the systematic methodology adopted for carrying out the demand projections:

The base for the current document are the projections given in the Plan document for the year

FY 2012-13 to 2021-22.

Demand projections were then refined for Power, Fertilizer, CGD and Petrochemical /Refining sectors in

order to project the most realistic demand scenario based on inputs from industry players and GoI

Sectoral demand growth rate beyond 2021-22 assumed (where ever required) based on assumptions

used in the ‘Plan Document’ and industry inputs

No constraints were applied in terms of natural gas price, supply and infrastructure

Regional distribution for demand were then projected

In the final step, the demand projections up to 2030 were consolidated considering all the above factors

2.3.3.2.2. Aggregated demand projections

The demand for individual sectors were worked based on past growth trends and then subsequently

aggregated to arrive at the total demand.

Table 9: Segment wise demand projections

Demand from Sectors

(MMSCMD) FY 2012-13 FY 2016-17 FY 2021-22 FY 2026-27 FY 2029-30

Power 86.50 158.88 238.88 308.88 353.88

Fertilizer 59.86 96.85 107.85 110.05 110.05

6 Website of PNGRB


City Gas 15.30 22.32 46.25 67.96 85.61

Industrial 20.00 27.00 37.00 52.06 63.91

Petrochemical/ Refineries 54.00 65.01 81.99 103.41 118.85

Sponge Iron/ Steel 7.00 8.00 10.00 12.19 13.73

TOTAL Demand 242.66 378.06 516.97 654.55 746.03

2.3.3.3. NITI Aayog

The projections provided in this section are based on the report “A Report on Energy Efficiency and Energy Mix

in the Indian Energy System (2030) Using India Energy Security Scenarios, 2047” released by NITI Aayog. The

numbers provided are based on possible scenarios, which NITI Aayog, in consultation with other stakeholders,

feels will be the most likely case. The scenarios have been developed using the tool India Energy Security

Scenarios (IESS), 2047.


The IESS, 2047 has been developed with a view that energy scenarios should be carried out together and not in

silos as was the practice earlier for each sectors. The IESS considers four possible scenarios or levels, which

are given as under

Level 1: Least effort scenario - This assumes that little or no effort is being made in terms of

interventions on the demand and the supply side, and represents a pessimistic outlook

Level 2: Determined effort - This describes the level of effort, which is deemed most achievable by the

implementation of current policies and programme of the government. It may be seen as the ‘current

policy’ with autonomous improvements

Level 3: Aggressive Effort - This describes the level of effort needing significant change, which is hard

but deliverable

Level 4: Heroic effort - This considers extremely aggressive and ambitious changes that push towards

the physical and technical limits of what can be achieved

The scenarios developed and provided in the report are considering Level 2 i.e. achievable as per implementation

of current policies of the Government.

The IESS, 2047 scenarios are based on certain assumptions relating to likely GDP growth, population, level of

economic activity, structure of the economy, urbanization, household occupancy etc. that would impact the

demand for energy.

The above analysis was carried out not only by considering the economic behavior in the Demand sectors, but

also the technology choices and fuel preference category, leading to significant impact as to how energy or

electricity is supplied to meet this demand. The conditions as they obtain in India have been factored in after

consulting sector specific players. The effect of energy efficiency has been considered in the demand side factors.

From the supply side, domestic resources meet each of the fuel demands first, and the balance is imported.

Within electricity, no preference is given to any technology on the supply side and past trends have been used

to project deployment of supply side technologies in 2030 and 2047.

2.3.3.3.2. Projections

With the above assumptions, the scenario developed for the three years have been provided wherein the primary

energy requirement has been projected as provided in the following table


Table 10: Energy mix as per current policy scenario

Primary Energy Supply (TwH) 2012 2022 2030 2047

Coal 3,284 5,792 7,773 13,401

Oil 1,929 3,093 4,429 7,137

Gas 574 1,017 1,325 2,068

Nuclear, Hydro and Renewables 245 629 935 1,968

Others 985 658 826 1,316

Total 7,017 11,189 15,286 25,890

From on the above projections, the share of oil and gas in the Primary Energy Demand has been estimated as

below:

Table 11: Share of Oil and Gas

Share of Oil and Gas (TwH) 2012 2022 2030 2047

Total supply 7,017 11,189 15,286 25,890

Oil 1,929 3,093 4,429 7,137

Gas 574 1,017 1,325 2,068

Percentage share of Oil 27% 28% 29% 28%

Percentage share of Gas 8% 9% 9% 8%

Based on the projections as per the level considered, it is observed that the share of oil and gas in the economy

will remain more at a constant level. The share of Oil and Gas is expected to increase proportionately with that

of the increase in requirement of primary energy.

2.3.4. Summary

The demand projections for Oil and Gas are based on methodologies developed by PPAC, P&NGRB

and NITI Aayog.

The projections developed by PPAC are bottom up projections developed based on the historical growth

rate of individual petroleum products. The total demand up to FY 2020-21 has been arrived at by

aggregating the individual projected demand of the products.

The projections given in the study conducted by PNGRB are based on the demand for Natural Gas

assessed for individual sectors namely Power, Fertilizer, City Gas Distribution and Petrochemical

/Refining sectors etc. which are dependent on Natural Gas. The individual demand of the industries is

then aggregated to arrive at the total demand of Natural Gas for the period up to FY 2029-30.

The projections developed by NITI Aayog are considering four probable scenarios, which may work out

from the current demand. The projections have been developed for primary energy and for three specific

years i.e. 2022, 2030 and 2047. The share of Oil and Gas has been worked out from the primary energy

in a top down approach. From the derived projections, it is observed that the share of oil and gas is

expected to maintain a constant level.


2.4. Power

2.4.1. Introduction

India is a country with more than 1.3 billion people accounting for almost ~18% of world’s population. India faces

a formidable challenge in case of providing adequate energy supplies to consumers at a reasonable cost. With

anticipated increase in India’s nominal GDP, the energy challenge thus is of fundamental importance. In addition,

in the last six decades, India’s energy use has increased by ~16 times and the installed electricity capacity by

around ~85 times. Nevertheless, India as a country has been suffering from energy poverty and pervasive

electricity deficits. In recent years, the country’s energy consumption has increased at a relatively fast rate due

to population growth and economic development and with the economy projected to grow, rapid urbanization and

improving standards of living for millions of Indian households, the demand is likely to increase significantly. The

supply challenge is also of such magnitude and there are reasonable apprehensions that shortages will be there

non-uniformly at various regions of the country even though on an all India level, the situation will be net surplus

in the future.

The power sector in India is at a crucial juncture now with several investments being undertaken by the public as

well as private sector. There has been an increasing private sector participation in the recent times. It was seen

in the 11th plan that 39% of conventional energy capacity addition was from the private sector while 35% from

the state utilities and around 26% from central power companies. Private sector also contributed around 59% in

the total capacity addition during FY 14-15. The emerging dynamics of Indian power sector market would require

industry players to realign their strategies and operating mechanisms to the changing sectoral trends.

Another area to highlight is the increasing share of renewable energy in the demand and supply of power in the

country. There has been a shift to renewable power in the recent times as the same constitutes around 19% of

the total installed capacity as on January 2018. The higher losses have also been a cause of concern for the

power sector and there is a need for efficient management of the infrastructure. The Indian electricity sector has

also seen an unprecedented growth in the last decade. The all India installed capacity has nearly doubled in a

period of ten years from ~144 GW in 2006 to ~330 GW in 20177. There has also been a steady growth in per

capita electricity consumption from 631 kWh in 2006 to 1122 8kWh in the year 2015-16.

Within the last half decade, the Indian power sector has witnessed a few success stories and has undergone

dynamic changes. However, the road ahead is entailed with innumerable challenges that result from the existing

gaps between what is planned and what the sector has been able to deliver. Demand forecasting thus poses a

significant role in trying to minimize these gaps.

2.4.2. Review of the existing methodologies

There are numerous methods, which are used today for forecasting the electricity demand. The methods have

evolved over time from being simplistic and reliant only on past trends to the current form wherein the impact of

different factors are modeled to arrive at the demand. The choice of a method is based on the nature of the data

available and the desired nature and level of detail of the forecasts required. An approach often used is to employ

more than one method and then compare the forecasts. This section provides the commonly used methods used

for electricity demand forecasting.

2.4.2.1. Trend method

This method falls under the category of the non-causal models of demand forecasting that do not explain how

the values of other variable affected the variable to be predicted. Here, the variable is expressed to be purely a

function of time, rather than by relating it to other economic, demographic, policy and technological variables.

Trend method is the most widely used forecasting method due to its ease of use and derivation of trend from the

7 CEA All India Installed Capacity as on 31st May 2017 8 CEA Final National Electricity Plan, 2018


available data. The inherent assumption in forecasting using trend method is that the future values will continue

to growth at the same trend.

The method ignores the impact of any other factor for example, the role of incomes, prices, population growth

and urbanization, policy changes etc. Therefore, it does not offer any scope to internalize the changes in factors

like effects of government policy, regulatory regimes, demographic trends, aggregate and per capita growth in

incomes, technological developments etc. This method is important as it provides a preliminary estimate of the

forecasted variable value and is accurate in the short term.

Review of the available literature on trend method highlights that such methods are easy to use indicators that

can provide a quick understanding. Though such techniques are relatively less common in academic literature,

however practitioners rely on them in many cases. The trend method is used by directly deriving the growth rates

from the available data e.g. sales or the growth rates are calculated from derived factors like specific consumption

of electricity.

2.4.2.1.1. Use of trend method in electricity demand forecasting

The trend method is the most commonly used method in the power sector. Every utility primarily uses the trend

method to forecast its sales and consumer growth. The acceptance of the method in India is quite high and the

results derived are also accurate and meets the intended usage of the forecast.

This method is used by CEA in developing forecasts for EPS. The PEUM method used by CEA is a combination

of trend and end use method. The 18th and 19th EPS of CEA relies on this method to forecast the consumption

of most consumer categories. In the 19th EPS, the energy consumption of each distribution utility was worked out

by PEUM method by forecasting for each categories using the past trends. The forecast was refined by

incorporating recent trends and policy initiatives.

In the draft NEP 2016, the projections were developed based on historical growth rates. In the case of draft NEP

2016, the past electricity consumption data from the year 1979-80 to 2013-14 was taken and growth rates were

derived for three different periods. Based on the review of the growth rates, suitable growth rates were assumed

to work out the future projections.

2.4.2.2. Time series method

A time series is defined to be an ordered set of data values of a certain variable. Time series models, are

essentially, econometric models where the explanatory variables used are lagged values of the variable, which

are to be explained and predicted. The intuition underlying time-series processes is that the future behavior of

variables is related to its past values, both actual and predicted, with some adaptation/adjustment built-in to take

care of how past realizations deviated from those expected. Thus, the essential prerequisite for a time series

forecasting technique is data for the last 20 to 30 time periods.

The difference between econometric models based on time series data and time series models itself, lies in the

explanatory variables used in the model. In an econometric model, the explanatory variables (such as incomes,

prices, population, GDP, temperature etc.) are used as causal factors and a relationship is derived while in the

case of time series models only lagged (or previous) values of the same variable are used in the prediction.

2.4.2.2.1. Commonly used time series methods

There are innumerable time series methods available in literature and mostly in academics. However, the

following two methods are most commonly used:

Exponential Smoothing

Auto Regressive Integrated Moving Averages (ARIMA)


Exponential Smoothing

Exponential smoothing is a technique that is usually applied to time series data, either to produce smoothed data

for presentation, or to make forecasts. Whereas in the simple moving average method the past observations are

weighted equally, exponential smoothing assigns exponentially decreasing weights over time. In other words,

recent observations are given relatively more weightage in forecasting method than the older observations.

Hence, it is preferred for short/ medium term forecasts.

Few of the exponential smoothing techniques used for forecasting are:

Simple exponential smoothing

The simple exponential smoothing considers only actual and forecasted value of the past to arrive at the

forecast for the next period by assigning weights. The model ignores the trend and seasonal components of

the time-series data and is represented by the following equations:

Ft+1 = α At + (1- α) Ft

Ft+1= forecasted value for period t+1

At = actual value for period t

Ft = forecasted value for this period

Where α determines the weight given to the most recent past observation and hence controls the rate of

smoothing or averaging.

Brown’s double exponential smoothing

Brown’s double exponential smoothing is a forecasting method similar to Simple Exponential Smoothing,

except that the smoothing constant in double exponential smoothing is derived by “re-smoothing” the single

smoothed constant from simple exponential smoothing model. Brown’s Double Exponential Smoothing is

meant to be implemented on data that show a linear trend over time and for the short term only.

Holt’s no seasonal trend smoothing

This model uses two smoothing constants, α and β to separately smooth the trend. It further adjusts each

smoothed value for the trend of previous period before calculating the new smoothed value. In addition to

the advantages of double exponential smoothing this is more flexible in that the level and trend can be

smoothed with different weights. This method does not account for the seasonality.

Holts-Winter’s multiplicative and additive exponential smoothing

This model extends Holt’s two-parameter model by including a third smoothing operation to adjust for the

seasonality. The equation assumes that the trend is additive and that the seasonal influence is multiplicative.

This method models trend, seasonality and randomness using as efficient exponential smoothing process.

However, the data requirement is greater than other methods

Auto-Regressive Integrated Moving Averages (ARIMA) Method

An autoregressive integrated moving average (ARIMA) model is a generalization of an autoregressive moving

averages (ARMA) model. These models are fitted into a time series to better understand the data or to predict

future points in the series. The model is generally referred to as an ARIMA (p, d, and q) model where the p, d

and q are integers greater than or equal to zero and refer to the order of the autoregressive, integrated and

moving average parts of the model respectively. This technique uses the historic values and the forecast errors

to predict the future values of a time-series data. ARIMA modeling can take into account trends, seasonality,

cycles, errors and non-stationary aspects of a data set when making forecasts. Since using ARIMA or any form

of this method involves complex modeling and calculations, a software is ideally used to generate forecasting

results.

http://www.investopedia.com/terms/s/seasonality.asp


The graphic below shows the time series option in SPSS software.

Figure 17: Snapshot a working tab in SPSS software

In the current study, the time series method will be used for short and medium terms forecasts using SPSS

software to generate forecast results. The time series methods in general is not ideal for long term forecasts as

the results are mostly under projected as lesser weightages are given in the longer period.

2.4.2.2.2. Use of time series in electricity demand forecasting

Worldwide, the method is being used for forecasting electricity demand in the short-term period. Due to the

complexity involved, the method is mostly limited to academia and most of the literature available is from

academic field. Some of the research papers have been quoted in this section to highlight the application of the

technique in electricity demand forecasting.

In a research paper titled ‘Very short-term electricity demand forecasting using adaptive exponential smoothing

methods’, the authors Abderrezak Laouafi, Mourad Mordjaoui, Djalel Dib [IEEE Explore, 2014], presented the

development of three new electricity demand-forecasting models, based on the use of the adaptive Holt's

exponential smoothing technique.

In a paper titled ‘Forecasting monthly peak demand of electricity in India—A critique’ by authors Srinivasa Rao

Rallapalli and Sajal Ghosh [ELSEVIER, 2012], the authors evaluated the monthly peak demand forecasting

performance predicted by CEA using trend method and compared it with those predicted by Multiplicative

Seasonal Autoregressive Integrated Moving Average (MSARIMA) model. They found that the MSARIMA model

outperforms CEA forecasts in all five regional grids in India. For better load management and grid discipline, this

study suggested employing sophisticated techniques like MSARIMA for peak load forecasting in India.

CEA in 19th EPS has the mentioned use of time series analysis along with end use methods. The time series

method has been used to derive growth indicators giving higher weightage to the recent trends to consider the

benefits of energy conservation initiatives and technological changes. However, in cases where no definite trend

emerged, weighted average (chronological or maximum AGR-maximum weightage) have been used for

forecasting electricity demand.

http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=%22Authors%22:.QT.Abderrezak%20Laouafi.QT.&newsearch=true

http://ieeexplore.ieee.org/search/searchresult.jsp?searchWithin=%22Authors%22:.QT.Mourad%20Mordjaoui.QT.&newsearch=true


2.4.2.3. Econometric methods

This is a standard quantitative approach for economic analysis that establishes a relationship between the

dependent variable and certain chosen independent variables by statistical analysis of historical data. The

relationship so determined can then be used for forecasting simply by considering changes in the independent

variables and determining their effect on the dependent variable. In the case of electricity demand forecasting,

the dependent variable is electricity consumption and independent variables can be population, consumer

indices, tariffs, weather variables, time etc.

The set of potentially important independent variables to be tested in the model can be selected based out of

experience and the influence of these factors is evaluated statistically. Normally the statistically relevant factors

are considered and included in the estimated demand function.

2.4.2.3.1. Commonly used econometric methods

Econometric methods can be classified based on the number of independent variables used in the equation. The

selection and choice of a variable is based on statistical analysis and the degree of relationship that is obtained.

Accordingly, there are two broad type:

Univariate econometric method

Multivariate econometric method

Univariate technique

In the univariate method, the dependent variable is expressed as a function of a single independent variable e.g.

population, time etc. and the relationship is developed.

Thus, in case of univariate relationship, one would have:

Y = f (POP)

Where,

Y = electricity demand

POP = population

A sample of univariate econometric model is shown in the table below wherein; only time variable (t) has been

used to develop an econometric relationship.

Table 12: Sample equations for univariate econometric model

Sl. Equation

1. Electrical Energy Demand = 29.01978 t3 - 966.933 t2 + 22171.28 t + 40252.89

2. Electrical Energy Demand = 0.71 t4 - 28.85 t3 + 486.18 t2 + 8221.48 t + 56971.26

In many cases, the univariate relationship is directly established by using only one variable and the equation so

developed is not further tested with additional variables as the single dependent variable alone provides a strong

relationship. In other cases, the dependent variable is tested with multiple variables initially, however only one

dependent variables shows a strong relationship and hence is used to develop the regression equation.

Multivariate technique

This technique is classified under multivariate techniques because it considers the causal-relationships that might

exist between multiple variables, demographic, economic, weather-related etc. and the variable that needs to be


projected. Taking time-series or cross-sectional/pooled data, causal relationships could be established between

electricity demand and other economic variables. The dependent variable, demand for electricity, can be

expressed as a function of various economic factors. For illustration, these variables could be population, income

per capita and weather related data etc.

Thus, one would have:

Y = f (X, W, POP)

Where,

Y = electricity demand

X = per capita income

W= Weather related parameter

POP = population

Several functional forms and combinations of these and other variables may have to be tried until the basic

assumptions of the model are met and the relationship is found statistically significant.

Inserting forecasts of the independent variables into the equation would yield the projections of electricity

demand. The sign and the coefficients of each variable, thus estimated, would indicate the direction and strength

of each of the right-hand-side variable in explaining the demand in a sector.

More specifically, a single equation regression model for each variable that needs to be forecasted can represent

these. The set of independent variable (i.e. variables that might affect the variable whose value is to be projected)

for each such model could vary. For example, while electricity consumption by the domestic category could be a

function (or affected by) of temperature of the region and GDP, consumption by Non-Domestic category could

be a function of per capita income and the number of consumers in that category.

The following table provides a set of sample equations for multivariate econometric method:

Table 13: Sample equations for multivariate econometric model

Sl. Equation

1. Electrical Energy Demand = - 7093.167+ 5.6084^E-4 * (Population) – 0.00254 * (GSDP Tertiary)

2. Electrical Energy Demand = - 1257.306 + 1.1082^E-4 * (Population) – 0.741 * (Electricity WPI) +

0.001785 * (GSDP Secondary)

Since the method considers the various external factors that may influence the dependent variable, it can be

used for short/ medium and long term.

Test for Goodness of Fit

The goodness of fit of a statistical model describes how well it fits a set of observations. Measures of goodness

of fit typically summarize the discrepancy between observed values and the values expected under the model in

question. This is often measured by a Pearson's chi-square test.

Pearson's chi-squared test uses a measure of goodness of fit which is the sum of differences between observed

and expected outcome frequencies (that is, counts of observations), each squared and divided by the

expectation. The test statistic being:


Where:

Oi = an observed frequency (i.e. count) for bin i

Ei = an expected (theoretical) frequency for bin i, asserted by the null hypothesis.

The expected frequency is calculated by:

Where:

F = the cumulative Distribution function for the distribution being tested.

Yu = the upper limit for class i,

Yl = the lower limit for class i, and

N = the sample size

The resulting value can be compared to the chi-squared distribution to determine the goodness of fit. The test for

Goodness of Fit shall be run for the approaches and finally, the model that yields the best fit shall be adopted.

2.4.2.3.2. Use of econometric technique in electricity demand forecasting

The econometric approach has seen significant developments over the past three-four decades. In the 1970s,

the main aim was to understand the relationship between energy and other economic variables.

In a paper titled ‘The structure of world energy demand’ published by the MIT Press, Cambridge, Massachusetts

in the year 1979, the author R. S. Pindyck succinctly captures the following:

“We have had a rather poor understanding of the response of energy demand in the long run to changes

in prices and income, and this has made it difficult to design energy and economic policies. By working

with models of energy demand rather different from those that have been used before and by estimating

these models using international data, we can better understand the long-run structure of energy demand

and its relationship to economic growth”.

The method is widely used across the world for forecasting the electricity demand for any licensee area, regional

or at a national level etc. There are numerous literature available wherein econometric technique have been used

to derive electricity demand.

M Ishiguro and T Akiyama in their paper titled ‘Energy demand in five major Asian developing countries:

Structures and Prospects’ published in 1995 have analyzed energy demand in five Asian countries, namely

China, India, South Korea, Thailand and Indonesia both at the aggregate level and the sector level using a simple

econometric model. They had used the model for forecasting energy demand in these countries up to 2005.

In a study titled ‘Regression Based Forecast of Electricity Demand of New Delhi [IJSCP, 2014] carried out by

Aayush and Agam Goel, IIT Delhi, the forecast of electricity of New Delhi area was worked out using three models

namely Multivariate Regression, Trend seasonality model and by ARIMA modeling. The paper discusses the

significance of climatic and seasonal factors on electricity demand as well as provides a comparison of the relative

accuracy of the models employed to derive the forecast.

In Draft NEP 2016, CEA has used univariate econometric forecasting technique to forecast the energy

consumption at an all India level. A 3rd and 4th degree regression equation was developed with energy

consumption as the dependent variable and time variables as independent variables. In the present case only

time variable (t) has been used to develop the relationship.


In the 19th EPS, electricity demand forecast by econometric method has been planned by CEA. The forecast will

be developed using multiple regression analysis and the electricity demand (in MU as well as MW) will be

estimated for all-India, Regions and state/UT for the period from FY 2016-17 to FY 2036-37.

2.4.2.4. End-Usage method

The end-usage approach attempts to capture the impact of energy usage patterns of various zones identified in

the state area. The end-usage model for electricity demand focuses the derivation of demand from the end use

of a product or area. For e.g. electricity demand from usage of new land in terms of Residential, Commercial,

Institutional and Industrial categories. The method takes in to account various approaches to derive the end use

demand e.g. by studying the land usage norms, load per area norms and the level/stage of

urbanization/development of these zones.

For example, the load in a zone (identified by the dominant consumer category) can be determined by considering

factors like the total area of that zone, Percentage occupancy (stage of settlement), the space coverage norm,

and the Floor to Space Index (FSI) to arrive at the effective usable area for that zone. Then this usable area is

multiplied to the approximate load per area to arrive at the load for that zone. It is further multiplied with the

utilization factor to estimate the effective load on the zone.

The following relation defines a tentative methodology for the land usage:

Lz = A x O x S x F x LA x UF

Lz = Utilized Load of a zone (MW)

A = Area of the zone (sq. km)

O = % of the area occupied based on the level of urbanization/development.

S = Space Coverage Norms/By-laws specified for each consumer category (% of the occupied area that

can be covered by any construction)

F = Floor to Space Index specified for each consumer category

LA= Load per area (MW per sq. km)

UF= Utilization factor for each consumer category

Similar estimations can be arrived at depending on data availability to determine the expected load that may

come into a system. When summed over different area in the state, aggregate energy demand for the system as

a whole can be ascertained. The method is used to refine the forecasts which are obtained by other methods, by

adding the expected loads.

The method is typically used in combination with other methods like time series, trends or econometric forecasts

to capture demands which might not be captured by past trends or be reflected by independent variables. The

additional demand arrived at by end sue method can be then suitable added to the base demand arrived at by

the primary methods.

The end-use approach is effective when new technologies and fuels have to be introduced and when there is

lack of adequate time-series data on trends in consumption and other variables. The approach demands a high

level of detail on each of the end-uses.

2.4.2.4.1. Use of end use method in electricity demand forecasting

The technique is used to estimate specific demand from particular categories or to estimate behavioral change

in a region over time. For example, the method is ideal to ascertain new demand from industrial activity in a

region or to ascertain penetration of energy efficiency. The results obtained greatly help in fine tuning the


forecasts developed from primary methods. The method has found application in estimating demand in specific

industries and sectors

In a report titled ‘India Energy Outlook: End Use Demand in India to 2020’ published by Ernest Orlando Lawrence

Berkeley National Laboratory in January 2009, the end use method has been used to estimate energy use in

agriculture, service and industry sector in India.

CEA in the 19th EPS has also used the end use methodology to estimate the demand from HT Industries above

1 MW. The electricity consumption for LT Industries and HT industries with demand less than 1 MW has been

projected on the basis of past trends and programme for development in future, as indicated by state authorities.

In the case of H.T. Industries with demand of 1 MW and above, projection is based on the information furnished

by end users/state utilities.

2.4.2.5. Benefits and challenges of using the forecasting methods

Method Benefits Challenges

Trend method Simplicity and ease of use in deriving the

forecasts

The results obtained in the short and

medium terms are fairly estimated

Skill and data requirement is very low

The models are more tractable and

changes are easily reflected

No additional data required other that

the historical data of the variable to be

projected

The method ignores possible interaction

of the variable under study with other

economic factors

The method cannot be relied upon for

long term projections

The method cannot incorporate or

capture directly the role of certain policy

measures/ economic shocks/ new

technology that might result in a change

Time series method

The method gives good estimates for the

short term period e.g. monthly forecasts

for a shorter period

The data requirement is lesser in sense

that only historical data of the single

variable to be predicted is required

Skill requirement is moderate and can be

used in excel also

The method do not describe cause and

effect relationship between variables

Estimates for long term period are not

reliable

The method cannot incorporate or

capture the role of certain policy

measures/ economic shocks/ new

technology that might result in a change

in the behavior

Econometric method The method considers the casual

relationship between variables

It provides good estimates of forecasts

across forecast horizon

The method is suitable for short, medium

and long term forecasts

The econometric method is flexible and

can be carried for differently for

short/medium and long term forecast by

selecting different independent variables

In some cases, no causal relationship

might be found upon which other

techniques have to be used

A drawback of the method is its

dependence on the forecast of the

independent variables

Skill requirement is high as knowledge of

statistics is required

The method requires more data in

comparison to other methods as

additional data of independent variables

is required.

End Use Method The method is used to estimate demand

from specific industries or to ascertain

new loads

The method may lead to a mechanical

forecasting of demands. Hence sector

knowledge and application of judgement

is important


It can be used to ascertain impact of new

technology when no historical data is

available or to understand behavioral

change

End use doesn’t require high skill in

gathering data and is not data intensive

The method faces challenges due to

non-cooperation from the target end

user

It also does not factor in the variations

in the consumption patterns due to

demographic, socio-economic, or

cultural factors

There are various other advanced methods and techniques that can be used for demand forecasting and which

are currently employed across the world. These include- Artificial Neural Networks, Fuzzy systems, simulated

annealing, Hybrids etc. As of now, only few firms and educational institutes like IITs have the requisite expertise

in using these techniques in India.

2.4.3. Demand forecasting

Central Electricity Authority (CEA) is the nodal agency that undertakes demand forecasting of power for the

country in all India level, regional level, state level as well as the distribution utility level. It conducts the Electric

Power Survey (EPS) periodically. CEA has been using PEUM for making the projections in power demand. In

the 18th EPS published in December 2011, Econometric techniques were also being used to validate the

forecasts. PEUM method that has been in use by the CEA is basically a bottom-up approach. The 19th EPS also

uses the PEUM to undertake the demand forecasting. The forecasting categories that are adopted include-

Domestic, Commercial, Public lighting, irrigation, industrial, railways etc.

2.4.3.1. Electric Power Survey (EPS) by CEA

The 19th Electric Power Survey Committee was constituted by CEA in June, 2015 and the Committee decided

to undertake the electricity demand forecast of Distribution Companies, Mega Cities and National Capital Region

and also the electricity demand forecast by Econometric Method. The Volume-1 of the 19th Electric Power Survey

Report, currently published, covers the electricity demand forecast of Distribution utility s/States/UTs/Regions

and for the country.

The basic objective of the 19th EPS is given as under:

To forecast the year wise electricity demand for each Distribution utility, State, Union Territory, Region

and all- India in detail up to the end of 13th Plan i.e. for the years 2016 -17 to 2021 -22. The category

wise forecast at the distribution utility level has also been provided.

To project the perspective year wise electricity demand for 14th plan i.e. from 2021-22 to 2026-27 and

the terminal years of 15th & 16th five year plans i.e. year 2031-32 & 2036-37.

2.4.3.1.1. Methodology adopted by CEA

Partial End Use Method (PEUM)

The following broad methodology has been adopted by CEA for the electricity demand forecast in the 19th EPS:

CEA has continued with the usage of PEUM as the base method for demand forecasting. The PEUM is

a combination of time series and end-use methods with higher weights given to short term so as to

consider recent trends.

Energy consumption of each Distribution utility was worked out by PEUM method by forecasting for each

categories using the past trends. The forecast was refined by incorporating recent trends and policy

initiatives.

The energy requirement of each Distribution utility was worked out by adding the corresponding

Transmission losses (intra-state) and Distribution losses considering the projected reduction in the losses


The total energy consumption of the state was worked out by aggregating the energy consumption

forecasted for each Distribution utility present in the state

The total energy required of the state was worked out by aggregating the energy required by the

Distribution utility s in the state

The Peak demand of the Distribution utility was worked out by assuming a suitable load factor which

was arrived based on trend analysis

The Peak demand of the State was obtained by applying suitable diversity factor to the peak demand of

the Distribution utility

The total energy projected for the state was then grossed up with inter-state/ regional transmission losses

to arrive at the ex-bus energy requirement for the state

Impact of Policy initiatives considered in the EPS

The power sector saw numerous policy initiatives being brought out by the Government in order to improve the

power situation in the country. The EPS has considered the policy measures in the final forecast by way of

suitable assumptions. The methodology adopted for major policy and programs are given as under in brief

Reduction in AT&C losses

The targeted trajectory to reduce losses is to about 13% by the year 2021-22 on an all India basis.

However the on ground AT&C loss of the Distribution utilities are on the higher side. Hence in the EPS,

the trajectory submitted by the Distribution utilities has been considered to arrive at the energy required.

DSM, Energy Conservation and Efficiency improvement programs

The impact of various efficiency programs like Standards and Labelling, Perform Achieve Trade (PAT)

scheme in Industries, LED distribution program etc. would lead to overall reduction in the energy

requirement and peak demand. The impact of such initiatives has been factored in the EPS.

Power for All (PFA) initiative

The Government of India PFA initiative aims to connect all un-electrified consumers by the year 2019

and to provide quality and uninterrupted power to all. Since the initiative would result in growth in the

demand for power for the shorter term, the same has been considered in the EPS.

Dedicated Freight Corridor

Based on the freight corridor and railway electrification plan, the impact of increased demand for power

has been considered.

Make in India

Since the initiative is to encourage manufacturing of products in the country, there will be increased

demand in the commercial and industrial sector. The same has been incorporated by the Distribution

utility s in their forecast that has been submitted to CEA.

Roof Top Solar programme

The policy for Renewables has set a target of 40 GW to be installed in the solar rooftop program by the

year 2022. This will be a substantial addition and would lead to some of the local demands being met

from the energy that will be generated locally leading to lesser demand on the grid.

Electric Vehicles (EV)

The growing push for EV (e-rickshaws, 2-3-4 wheelers etc.) and the green mobility programs should lead

to an increase in power demand requirement. However, as per the estimates of CEA, the net addition

would be under manageable limits even if the targets of the Government for EVs are met.


Captive Power pants

From the historical trend, it has been observed that the CAGR of energy consumed from captive power

plants from the period 2010-11 to 2014-15 is about 8.7%. It has been assumed that in the future, there

will be improvement in the supply from the grid due to which the demand for captive plants will decrease.

About 15% up to 2021-22 and 25% by 2026-27 could be replaced by electricity from the grid gradually.

Methodology adopted by CEA for individual categories

The projection for the period up to 2026-27 has been carried out based on various consumer categories. The

methodology adopted in each is briefly given in the following:

Domestic

The forecast for the domestic category has been arrived at by considering the number of consumers

during the mid-year for each year and the specific consumption (average power consumption per

consumer). Considering the PFA programme, all the households have been considered by the year 2018-

19. The specific consumption has been worked out using historical trends and with suitable assumptions

for increased supply hours, improvement in economic conditions, efficiency programs etc. A marginally

increasing trend has been considered.

Commercial

The consumption for the category has been estimated by considering the number of consumers during

the mid-year for each year and the specific consumption (average power consumption per consumer).

Public Lighting and Public Water Works

The consumption in the category has been projected based on the connected load and the average

consumption of connected load or supply hours. The estimations are totally assumptions based as the

inputs like connected load and supply hours have been worked out as per assumptions.

Irrigation

The power demand for irrigation has been worked out by projecting the number of pumps in mid-year,

the average capacity of each pump and the average consumption per load or hours of operation with

assumptions. The demand for lift irrigation has been considered based on the end use worked out based

on the programme of development of lift irrigation schemes indicated by Distribution utility s/ State

Authorities.

Industry

For projecting the demand from the industrial sector, CEA relied on the historical trends and the end use

data. For LT Industries and HT Industries with less than 1 MW load, the projection were based on

historical trends. For HT industry with more than 1 MW, the end use data submitted by States/ Distribution

utilities etc. were used to arrive at the projections.

Railway Traction

The estimates for Railway have been carried out based on the existing requirement and the track

electrification programme envisaged by Ministry of Railways/Railway Board.

Bulk Non-Industrial HT Supply

The bulk demand for major institutions, Government and Private establishments, defense etc. has been

considered as per end use demand forecasted by the Distribution utility s. Bulk supply also includes

supply to Distribution Licensees, which have been considered as point load by the main Distribution

Licensee, open access consumers and SEZ's.


Methodology adopted for other major parameters

Once the projections for individual categories were developed, to arrive at the peak demand and to determine

the overall energy requirement, major parameters like Load Factor, Diversity Factor, T&D Loss etc. were suitably

projected and considered. The section below provides a brief about the methodologies adopted:

Transmission and Distribution loss

The T&D loss has been considered as per the trajectory submitted by the Distribution utilities. However,

the Distribution utilities were not able to segregate the transmission and distribution losses at their end,

hence the intra state Transmission losses were also considered with the loss of the Distribution utilities.

The total Intra State T&D loss for the state will be the sum of the Intra State T&D loss considered for

each Distribution utility. The interstate T&D loss has been added to the energy requirement of the state

to arrive at the energy requirement for the state at the ex-bus of generating end.

Load factor

As given in the EPS, the load factor depends on the consumers mix and the pattern of utilization of the

load. Before estimating the future load factors for individual states, CEA carried out a detailed analysis

to ascertain the influence of load mix on the load factor. However the details of the analysis and the

results are not mentioned in the EPS.

Diversity factor

The energy requirement for the country has been worked out by aggregating the energy requirements of

all the states. Peak demand on all India basis has been calculated by taking diversity factor among the

regions. CEA has provided the region specific diversity factor arrived as per trend analysis.

2.4.3.1.2. Demand Projections as given in EPS

The 19th EPS provided projections for All-India, Region wise, State and up to the Distribution utility level for the

period FY 2016-17 to FY 2026-27. The projections included energy consumption, energy requirement, peak

demand, T&D losses etc. The projections for the period also includes category wise consumption at the

Distribution utility level.

For the period beyond 2026-27, CEA has provided perspective demand projections for the terminal years

2031-32 and 2036-37 up to the state level.

Electricity demand projection from 2016-17 to 2026-27

The electricity demand in terms of energy consumption has been forecasted category wise for each Distribution

utility for each state and then aggregated to arrive at the regional and all India level forecast.

Table 14: Projections at All-India level for the period FY 2016-17 to FY 2020-21

FY 2016-17 FY 2017-18 FY 2018-19 FY 2019-20 FY 2020-21 FY 2021-22

Electrical Energy

Consumption (MU) 920,837 994,382 1066,989 1144,579 1222,286 1300,486

Electrical Energy

Requirement (MU) 160,429 1240,760 1317,962 1399,913 1483,257 1566,023

Peak Electricity

Demand (MW) 161,834 176,897 188,360 200,696 213,244 225,751

T&D losses 20.65 19.86 19.04 18.24 17.59 16.96

Derived load factor (%) 81.85 80.06 79.87 79.62 79.40 79.18


Table 15: Projections at All-India level for the period FY 2022-23 to FY 2026-27

FY 2022-23 FY 2023-24 FY 2024-25 FY 2025-26 FY 2026-27

Electrical Energy

Consumption (MU) 1380,197 1463,505 1551,066 1644,635 1743,036

Electrical Energy

Requirement (MU) 1650,594 1739,618 1836,001 1939,111 2047,434

Peak Electricity

Demand (MW) 238,899 252,288 266,844 282,418 298,774

T&D losses 16.38 15.87 15.52 15.19 14.87

Derived load factor (%) 78.87 78.71 78.54 78.38 78.23

Table 16: CAGR of the projections

CAGR (%) FY 2010-11 to

FY 2015-16 (Actual)

FY 2015-16 to

FY 2016-17

FY 2016-17 to

FY2021-22

FY 2021-22 to

FY2026-27

Electrical Energy

Consumption (MU) - 8.00 7.15 6.03

Electrical Energy

Requirement (MU) 5.28 6.42 6.18 5.51

Peak Electricity

Demand (MW) 4.63 9.32 6.88 5.77

Perspective Electricity demand projection for FY 2031-32 and FY2036-37

The perspective electricity demand in terms of energy consumption for the terminal years of FY 2031-32 and

FY 2036-37 has been forecasted for each state and then aggregated to arrive at the regional and all India level

forecast.

Table 17: Perspective electricity demand for FY 2031-32 and 2036-37

Electrical Energy Requirement CAGR (%)

FY 2026-27 FY 2031-32 FY 2036-37 FY 2026-27 to

FY 2031-32

FY 2031-32 to

FY 2036-37

Electrical Energy

Consumption (MU) 1743,036 2192,305 2672,302 4.69% 4.04%

T&D Losses (%) 14.87% 13.37% 12.37% - -

Electrical Energy

Requirement (MU) 2047,434 2530,531 3049,478 4.33% 3.80%

Peak Electricity Demand

(MW) 298,774 370,462 447,702 4.40% 3.86%

Derived Load Factor (%) 78.23% 77.98% 77.76% - -

Comparison between 18th EPS and 19th EPS projections

The 18th EPS was published in December 2011 and projections were developed based on the prevailing

situations at that time and with a view that demand will increase at a sustained rate over the years. However, the

actual growth of electrical energy requirement and peak electricity demand was less as compared to the


projections. The variations in the years in the shorter term was ~ 1% between the forecast and the actual.

However, over the period, the difference increased and there was greater variation towards the terminal years.

The table below provides a comparison between the 18th EPS projections, the actual demand and the 19th EPS

projections:

Table 18: Comparison of Energy Requirement between 18th and 19th EPS

Year

18th EPS

projections

(MU)

Actual

Energy

requirement

(MU)

Difference

(%)

18th EPS

CAGR (%)

Actual CAGR

(%)

19th EPS

Projections

Difference

18th EPS vs

19th EPS (%)

2010-11 870,831 861,591 (1.06%) - - - -

2011-12 936,589 937,199 0.07% 7.55% 8.78% - -

2012-13 1007,694 995,557 (1.20%) 7.59% 6.23% - -

2013-14 1084,610 1002,257 (7.59%) 7.63% 0.67% - -

2014-15 1167,731 1068,943 (8.46%) 7.66% 6.65% - -

2015-16 1257,589 1114,408 (11.39%) 7.70% 4.25% - -

2016-17 1354,874 7.74% 1160,429 (14.35%)

2021-22 1904,861 - - - - 1566,023 (17.79%)

2026-27 2710,058 - - - - 2047,434 (24.45%)

Table 19: Comparison of Peak demand between 18th EPS and 19th EPS

Year

18th EPS

projections

(MW)

Actual

Energy

requirement

(MW)

Difference

(%)

18th EPS

CAGR (%)

Actual CAGR

(%)

19th EPS

Projections

Difference

18th EPS vs

19th EPS (%)

2010-11 122,287 122,287 0.00% - - - -

2011-12 132,685 130,006 (2.02%) 8.50% 6.31% - -

2012-13 143,967 135,453 (5.91%) 8.50% 4.19% - -

2013-14 156,208 135,918 (12.99%) 8.50% 0.34% - -

2014-15 169,691 148,166 (12.58%) 8.63% 9.01% - -

2015-16 183,902 153,366 (16.60%) 8.37% 3.51% - -

2016-17 199,540 8.50% 161,834 (18.90%)

2021-22 283,470 - - - - 225,751 (20.36%)

2016-27 400,705 - - - - 298,774 (25.44%)

The comparison of projections for 18th and 19th EPS highlight that there has been a reduction in the forecast in

19th EPS as compared to that of 18th EPS. The current base year projections have been reduced by about ~ 15%

and for peak demand, the projections have come down by ~ 19%. The reason for such a reduction is considering

the actual situation in the country which didn’t pick up as projected in the 18th EPS. The projections in the 19th

EPS are also considering all the initiatives that the government as rolled out in the recent past.


2.4.3.2. Draft National Electricity Plan 2016

During the release of the Draft NEP 2016, the 19th EPS was under draft stage. Hence for undertaking generation

and transmission expansion planning studies, demand projections were developed.

2.4.3.2.1. Methodology 1 – using historical growth rates and aggregating of demand of individual State/UT

The forecast of electricity consumption of all the States/UTs has been done by adopting suitable growth rate,

from which the electrical energy requirement of each States/UT was obtained by adding T&D loss of the State/UT.

Electrical energy requirement at all-India level was obtained by aggregating the electrical energy requirement of

all the States/UTs.

Past electricity consumption data from the year 1979-80 to 2013-14 was taken from General Review Report of

CEA. The effect in electricity consumption on account of Power for All, DSM, Energy Efficiency etc. were suitably

incorporated. However, no detailed methodology was provided regarding the same.

The growth rates were calculated for three periods

Groups Data period Reason

I Electricity consumption from 1979-

80 to 2013-14 To observe overall effect of pre and post Electricity Act

II Electricity consumption from 2000-

01 to 2013-14

Covers the period immediately preceding the enactment of

Electricity Act, 2003 and period of reforms

III Electricity consumption from 2009-

10 to 2013-14 Covers the period of latest trends of growth

Based on the review of the growth rates, suitable growth rates were assumed to work out the future projections.

The forecasts for each state/UT was worked out and then added to arrive at the all India energy consumption.

The actual T&D loss considered for the year 2013-14 was taken and a reducing trend was assumed. The

State/UT wise electrical consumption was grossed up with the T&D loss to arrive at the electricity requirement.

The load factor was also assumed and a decreasing trend was considered for future years. The peak demand

on all India basis was worked out using the load factor considered.

2.4.3.2.2. Projection using historical growth rates

The projections were developed as per the methodology mentioned to arrive at the electrical energy required and

peak demand on an all India basis.

Table 20: Energy requirement and peak demand an all-India basis without DSM, EE measures

Year Electrical Energy

requirement (MU) CAGR (%) Peak Demand (MW) CAGR (%)

2016-17 1230264 - 170950 -

2021-22 1748251 7.28 244753 7.44

2026-27 2335987 6.00 329998 6.15

The draft NEP 2016 also calculates the decrease in energy requirement and peak demand due to measures such

as DSM, Energy Efficiency and conservation factors. Since the detailed calculation has not been provided

regarding the assumptions taken, hence it is difficult to ascertain the how the values have been arrived at.


Table 21: Energy requirement and peak demand an all-India basis with DSM, EE measures

Year Electrical Energy

requirement (MU) CAGR (%) Peak Demand (MW) CAGR (%)

2016-17 1204264 - 163673 -

2021-22 1611251 NA 235317 NA

2026-27 2131987 NA 317674 NA

2.4.3.2.3. Methodology 2 – using Regression method

In the second method, econometric technique was used to forecast the energy consumption at an all India level.

Since it’s a top down approach, the forecast arrived at is not after aggregation of demand from states but is at

the all India level. 3rd and 4th degree Regression equation was developed with energy consumption as the

dependent variable and time variables as independent variables.

Table 22: Regression equations developed

Sl. Equation Coefficient of determination

1.

Electrical Energy Consumption

= 29.01978 t3 - 966.933 t2 + 22171.28 t + 40252.89

R2 = 0.98646

2.

Electrical Energy Consumption

= 0.71 t4 - 28.85 t3 + 486.18 t2 + 8221.48 t + 56971.26

R2 = 0.98745

2.4.3.2.4. Projections using Regression method

The using the equations derived, the electrical consumption for every year at an all India level was calculated up

to the year 2026-27. The electrical energy requirement was calculated by grossing up with the derived T&D loss.

For comparison, the table also provides the projections arrived earlier using the historical growth rates.

Table 23: Forecasts developed using both the methodologies

Year Based on Growth Rate 4th degree Regression

equation

3rd degree Regression

equation

Electrical Energy

Consumption (MU)

Electrical Energy

Requirement (MU)

Electrical Energy

Consumption (MU)

Electrical Energy

Requirement (MU)

Electrical Energy

Consumption (MU)

Electrical Energy

Requirement (MU)

2016-17 973237 1230264 966852 1222198 939221 1187270

2017-18 1059641 1328124 1046093 1311144 1006798 1261893

2018-19 1151273 1430242 1132498 1406917 1078884 1340312

2019-20 1246443 1534152 1226566 1509686 1155652 1422404

2020-21 1345034 1639854 1328813 1620078 1237277 1508478

2021-22 1448067 1748251 1439773 1738238 1323933 1598384

2022-23 1556000 1859917 1559996 1864693 1415794 1692326

2023-24 1668763 1974589 1690048 1999775 1513035 1790321

2024-25 1786027 2091826 1830514 2143930 1615828 1892386


2025-26 1908486 2212333 1981996 2297546 1724349 1998880

2026-27 2036057 2335987 2145110 2461105 1838771 2109639

2.5. Summary

The findings in the chapter are summarized below:

Sector Duration Projection by Method Other details

Coal upto FY 27 CEA Top down Coal demand derived based on

historical growth rates

upto 2047 NITI Aayog Top down

Demand and supply side forecast

Four scenarios developed

Different growth rates for

scenarios considered

Renewable

Energy upto 2022 MNRE Top down

Policy decision to install renewable

capacity of 175 GW by 2022

upto 2027 CEA Top down 275 GW of renewable capacity by

2027 in final NEP’18

Oil & Gas Released

yearly PPAC/ MoP&G Bottom up

Demand of individual petroleum

products derived using historical

growth rates

2022, 2030

and 2047 NITI Aayog Top down

Share of O&G in primary energy

derived for three specific years

Four scenarios were also

developed

Electricity

Upto

FY2027,

FY 2032 and

FY 2037

19th EPS by CEA Bottom up

Forecasts developed at Utility,

State & National level across

consumer categories

Partial end use and econometric

method

Upto FY

2027 NEP by CEA Top down

National level supply side planning

Forecasts of 19th EPS is used to

undertake planning

The review of the sectors was focused on identifying the specific methodologies used. It was observed that a mix

of top down and bottom up methodologies are being used and most of the methodologies relied on using historical

growth rates, assumptions and scenarios.


3. Linkage between primary energy and power demand

3.1. India: Current energy scenario

The growth in India’s economy has led to an ever increasing demand for energy in the country. As per the India

Energy Outlook report 2015 of International Energy Agency (IEA), the primary energy demand of India has

doubled since the year 2000. Even though India has consistently improved upon the energy intensity, it is still

below the global average and in comparison with developed nations. The tables below highlight the energy

intensity for India and that of other nations.

Table 24: Energy Intensity9 Energy is linked to the standard of living of citizens of any

country. With nearly 304 million Indians without access to

electricity and energy access still a distant dream of many

millions in the country, it may be acknowledged that the

country has a long way to go on securing its energy security

objective. For India with an ever growing population and with

an aim to be a developed country, it is of utmost importance

to have focused efforts towards the energy sector. The

recently published Draft National Energy Policy (NEP) aims

to chart the way forward to meet the Government’s recent

announcements in the energy domain. The Government

plans to electrify all the census villages, and achieve

universal electrification with 24x7 electricity by the year

2022. The share of manufacturing in our GDP is to go up to 25% from the present level of 16%, while the Ministry

of Petroleum is targeting reduction of oil imports by 10% from 2014-15 levels, both by 2022. As of July 2017,

around 18% of the villages are still to be electrified. Other notable targets are to achieve reduction of emissions

intensity by 33%-35% by 2030 over 2005, achieving a 175 GW renewable energy capacity by 2022, and to have

a share of non-fossil fuel based capacity in the electricity mix at above 40% by 2030.

India at the current situation is at the cusp of a change with multiple factors and policy reforms affecting demand

and supply scenario. The pace has picked up and needs to be sustained over time. The country at present offers

tremendous opportunities and scenarios. The road ahead will depend on which scenario will be followed.

3.2. Historical linkage between Primary Energy and Power demand

Primary energy consists of the energy derived from primary sources like coal, oil, natural gas etc. whereas

electricity is a secondary energy formed after conversion from primary energy sources. Primary energy can be

conventional and renewable. The use of primary energy as a measure ignores conversion efficiency. Thus forms

of energy with poor conversion efficiency, particularly the thermal sources, coal, gas and nuclear are overstated,

whereas energy sources such as hydro which are converted efficiently, while a small fraction of primary energy

are significantly more important than their total raw energy supply may seem to imply.

Historical data highlights a linkage between primary energy and electricity demand and over the period, the share

of electricity has been growing at a steady rate. The figure below highlights the trends in consumption of Primary

Energy and specific to electricity as such.

9 World Energy Outlook 2011

Country Energy Intensity

(Kgoe/US$)

United Kingdom 0.102

Germany 0.121

Japan 0.125

Brazil 0.134

USA 0.173

China 0.283

India 0.191


Figure 18: Consumption of Energy and Share of Electricity in Primary energy consumption10

From the figure above, it is observed that there has been a steady rise in the share of electricity consumption in

the past ten years from around 9.90% in the year 2006-07 to about 12.75% in the year 2015-16. Considering the

trend it can be safely extrapolated that the trend will continue in the future also. With the recent push from the

Government in the energy sector and with the targets of the Government for providing access and quality power,

the share is bound to increase.

From the supply side, around three quarters of India’s primary energy needs is met by fossil fuels, amongst which

coal is the primary source followed by petroleum. With the increase in demand, the dependence on coal has also

increased with coal contributing to 44% of the energy mix in 2013 from about 33% in the year 2000 as shown in

the figure11 below. Over the same period, oil and natural gas has maintained a constant share of ~ 30% of the

energy mix.

On the demand side, since nearly 70% of the electricity generation capacities are still linked with coal as a primary

source, the increased requirement of coal can be attributed to the electricity demand which has also consistently

grown over the years. The demand for electricity is bound to increase over the years and most of it will be met

with the coal only. With current all India PLF of plants at around 57.43%12 as on June’17, it is important to note

that the increasing demand will be met first by utilizing the available capacity before new plants are being planned.

10 Energy Statistics 2017, MoSPI 11 India Energy Outlook, IEA 12 CEA Power Sector, June’17

0.00%

2.00%

4.00%

6.00%

8.00%

10.00%

12.00%

14.00%

0

1000000

2000000

3000000

4000000

5000000

6000000

7000000

8000000

9000000

2006-07 2007-08 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16

% S

hare

of E

lectr

icity C

onsum

ptio

n

Consum

ptio

n in

GW

h

Consumption of Electricity (GWh) Consumption of Primary Energy (GWh)

% of Electricity w.r.t Primary Energy

33%

25%5%

1%

34%

2% 2000

Coal

Oil

Natural Gas

Nuclear

Biomass

Other Renewables

44%

23%

6%

1%

24%

2% 2013

Figure 19: Primary demand by fuel


Also with increasing share of renewables in the generation capacity and Government commitment in the COP21

regarding cutting of emissions, the share of fuel in the primary energy pie may undergo some changes.

3.3. Effect of New influencers on the Power sector

There are several factors which impact the overall electricity demand in any region. Traditionally, these factors

have been impacting the demand growth of electricity over the years. A growth in population directly impacts

demand for electricity or any variation in weather related parameters

impact the demand in any region. Therefore these factors are looked into

in detail and analyzed while developing the forecasts for any region or

utility. In the present study also, the impact will be studied while

undertaking the forecast.

Other than the traditional factors, various additional factors have also been

identified and it is anticipated that these new influencers would affect the

demand-supply scenario of power in the country in future. Hence it is

important that such factors are looked in in the present study.

The influencers have been categorized into nine broad categories and is

highlighted in the figure below:

Figure 20: New influencers expected to impact electricity demand- supply

These identified new influencers would affect the future demand supply scenario in the country and also in the

end impact the primary energy requirement in the country. The effect of the new influencers are provided below:

Policy interventions

Interventions as related to the policy guidelines of the government can have a significant impact in the

demand-supply scenario of power in the country. The interventions have a direct impact on the sectors.

In the past few years, the focus has been on promoting renewable energy especially solar followed by

wind. Accordingly the impact is visible in terms of capacity additions and stakeholder interest that are

being developed.

Traditional factors which

impact demand

Economic development

Population growth

Infrastructure development

Industrial growth

Weather related factors

Network parameters


Power for All and Saubhagya scheme

The PFA scheme is expected to improve the entire value chain of the power sector and ensure reliable

and uninterrupted electricity supply to all households, industries and commercial establishments. The

target is to provide reliable and continuous power supply for all by the year FY2018-19. Power supply to

irrigation and unconnected households are also supposed to increase within that time frame. The

Saubhagya scheme is targeted more towards ensuring last mile connectivity in urban and rural areas

and to release electricity connections to electrified households in the country. With such changes

happening, the overall demand and category wise consumption patterns may increase. In order to

capture the revised consumption pattern, projection of category wise consumption will be required at a

circle/ sub-divisional level. The demand forecasting techniques need to incorporate specific trends at the

consumer level.

Rural electrification

The scheme under DDUGJY focuses on reforms in the rural power sector through separation of feeder

lines (rural households and agriculture) and strengthening of distribution as well as transmission

infrastructure. As per the data available from MoP, as on August 2017, 3276 villages are to be electrified

out of the targeted 18,452 villages i.e. ~ 18% of the villages are to be electrified as on date. These

electrification is expected to impact the demand of electricity in the rural areas. Strengthening and

augmentation of infrastructure can also impact the consumption patterns and the latent demand from the

rural areas will be realized. Since these loads were not considered historically, in future it will impact the

demand. With more infrastructure, the actual losses may also vary from the targets and will have an

impact on the overall supply that will be required in future.

Self-generation

Earlier self-generation measures were encouraged owning to high tariffs, unreliability of power supply

and some enabling regulatory provisions. Presently, there is a reform push for self-generation and also

as a means for areas wherein grid has not reached. Such measures (e.g. solar rooftop) may make the

consumption patterns of the households quite variable and intermittent. Increasing tariffs may make

consumers shift to a more cost-saving self-generation measures. The consumer of the past may become

a pro-sumer. With evolving and enabling regulatory provisions of net metering and upcoming focus on

storage solutions, a major demand from the DISCOMs is expected to be reduced in future.

Energy efficiency

Energy efficiency is expected to reduce the electricity demand and peak load. However, research has

suggested that the behavior of households are different then what is expected while designing the

policies. The effect is known as the rebound effect in energy efficiency. Similar situation may also happen

in India though it is early to arrive at any conclusions. Also, though energy efficient appliances are

purchased but are not used instantly and most of it are kept as spare. So the impact is no directly related

to the purchase of an appliance but pertaining to its usage. Hence, an overall assessment will have to

be made to ascertain the impact of energy efficiency.

Megatrends

Various evolving megatrends can be seen in the economy today. Some of these trends can have an

influence in altering the demand-supply scenario. Some of these megatrends include population growth

and other demographic changes (e.g. increase in share of young people changing the population

structure), social changes, economic growth and development, rapid urbanization (e.g. penetration of

international lifestyles among middle classes), shift of population from rural to urban areas and increase

in the living standards of the people.

Competition

With the changing dynamics of the power sector, competition is set to rise. Few features of competition

include open access, parallel licensees, deemed licensees (Railways, SEZs) and local decentralized


energy systems. With consumers shifting to open access, the power demand of the distribution licensees

would change. Also, with increase in competitive environment owing to better pricing and procurement

policies, the distribution licensees would be required to plan their procurements in an effective way.

Renewable energy

The total installed capacity of renewable power is 58.3 GW as on June 2017 and it is projected to achieve

an installed capacity of 175 GW by the year 2022. The increase in capacity of renewables will affect the

demand supply scenario in the country and also affect grid operations. Due to the inherent seasonal/daily

variation in generation from the renewable sources, consumption patterns of consumers may be

impacted. For example, the generation of wind power is highest during the summers and solar power is

directly available only during the daytime. With new technologies like solar roof-top and expected

increased penetration of Electric Vehicles (EV), the consumption behaviors may undergo changes.

Technology – EV, Storage solution, Smart Grid, RE Hybrids etc.

Technology is evolving and continue to make its mark in the power sector. In the near future, technologies

such as smart grids, advanced metering, energy storage solutions, RE hybrids and electric vehicles are

expected to play a greater role in the power sector in the country. The increase in efficiencies due to

technological interventions and subsequent fall in prices of energy storage solutions may change the

demand patterns of consumers by increasing the scope for self-generation. The storage solutions is also

expected to play a critical role in grid stability with increased penetration of renewables in the Grid.

Further, the complimenting nature of generation from solar and wind sources on an intraday as well as

on annual basis presents RE hybrids as a potential solution to the large scale grid integration of

renewables. The Government’s evolving policies aim at EVs playing a greater role in mobility as well as

storage solutions and for developing the EV charging infrastructure. The advent of the technologies

highlighted is expected to impact the power demand in the future. However the exact impact on power

and primary energy can only be gauged based on the strategic directions which are considered now.

The new influencers identified in this chapter is expected to change the existing demand supply scenario and

hence it is essential that the impact is captured in the forecast. The subsequent chapter deals in detail about

overall alternate methodology proposed for the study.

3.4. Effect of New Influencers on the Primary Energy

Since there is a direct linkage between the power demand and primary energy, any impact in the power demand

will affect the primary energy in terms of change in the primary energy mix of the future or in the overall

requirement. With increasing demand over time, there will be an overall and continuous increase in the primary

energy requirement. But the new influencers identified will affect the mix of the primary energy and how the

increased energy requirement will be met in the future. The change in the share of energy sources will mean an

interplay between the sectors.

Considering the case of increased EV penetration expected in the future, there will be demand for power to meet

the charging needs. During day time, more power will be required to meet the increased day time commute. This

demand for power may be met by increased generation from renewable sources e.g. solar which peaks during

day time. In case, the renewable fueled charging stations draw the requisite mount of power from non-

conventional sources during the substantial period of the day, the demand for power from conventional sources

would be less in future thereby lesser coal will be required. This highlights the effect of one sector on another

wherein an expected increased demand is being met by an increase in share of renewables thereby reducing

the dependence on the existing dominant sources. This interplay between the sectors has a direct impact on the

primary energy mix and the requirement of energy in the future.


3.5. Expected linkage between Primary energy and power demand

In June 2017, NITI Aayog has released the draft Energy Policy, 2017 wherein the future of India in terms of

Energy were outlined. As per draft policy “the energy demand of India is likely to go up by 2.7-3.2 times

between 2012 and 2040 with the electricity component itself rising 4.5 fold”.

NITI Aayog has developed the India Energy Security Scenarios 2047 wherein projections have been done both

from a supply and demand perspective considering possible scenarios like as business as usual and ambitious

scenarios. The as usual scenario is considering the current conditions and the ambitious scenario considers

future efficiency improvements and is cleaner and sustainable.

Some of the assumptions considered in developing the scenarios are

GDP of the economy is assumed to grow at a CAGR of 8% between 2012 and 2040

The Population will grow from 1.2 billion to 1.6 billion

Attainment of development ambitions like Power for All by 2022, Housing for All by 2022, 100 Smart

Cities, 175 GW Renewable capacity by 2022

In developing the two scenarios, different efficiency levels and technological measures were considered

which will lead to variation in the final projections

The supply side projections are for primary energy sources like Coal, Oil, Gas, Renewable and others etc.

whereas the demand side projections are considering various broad sectors like Industry, Buildings,

Transportation, Telecom, Cooking, Pumps and Tractors etc.

Based on the projections, the share of electricity in energy demand has been arrived as given in the following

Table 25: Share of electricity in primary demand

2022 2040

Scenario As usual Ambitious As usual Ambitious

Share of electricity in primary

energy demand 19.6% 20.4% 23.2% 26.1%

The supply scenario has also been developed considering that the energy demand works on the principles of a

reduction in import dependence, and a transition towards cleaner and sustainable supply options. The future

scenarios have also considered increased penetration of renewables, lower dependence on oil and coal,

possibilities of storage facilities, improvement in gas situation etc.

The following table provide the primary energy supply for three years namely 2012, 2022 and 2040.

Table 26: Share of primary energy under two scenarios in 2022 and 2040

Sources 2012 2022 2040

(TWh) As usual % Share Ambitious % Share As usual % Share Ambitious % Share

Coal 3281 6021 50% 5529 49% 11320 50% 8433 44%

Oil 1936 3024 25% 2762 24% 6036 27% 4883 25%

Gas 570 1018 9% 1016 9% 1762 8% 1788 9%

Renewable &

Clean energy 266 797 7% 823 7% 2010 9% 2602 13%


Others 1060 1108 9% 1152 10% 1351 6% 1626 8%

Total 7113 11968 11282 22479 19332

The scenarios developed highlights the possible change in energy mix which may happen under current

conditions and with more efforts i.e. the ambitious condition. In the shorter period upto 2022, the will be increase

in share of coal from the current situation even if ambitious scenario were to take place. This is majorly to meet

the increased electricity demand in the country. Post year 2022, if the share of renewables increase in the primary

energy, then there is a possibility of coal share going down. The other energy sources namely Oil and Gas will

hold their share in the fuel mix even with both the scenarios.

The projections and expected linkages highlighted in the sections above for various sectors are based on

assumptions and scenarios. The scenarios being developed have been aggregated based on current demand

supply situation, considering the various initiatives that the government has planned to implement and with some

foresight into how the sector might evolve into the future. The reports available in the public domain published by

Government and other agencies over time have projected the energy requirements of the country. However, with

time, the assumptions change and scenarios have developed while maintaining with the stance that from the

demand side there will be growth in the primary energy and electricity consumption in the country.

On the supply side, there is expected to be variation in the energy mix and share of energy from the primary fuel.

World over, there has been a trend towards reduction of dependence on fossil fuels and move towards a regime

where renewables have a greater share. However, India traditionally being dependent on fossil fuels will have

tougher task at hand in reducing the share of fossil fuels. While recent efforts of initiatives towards renewable

energy, energy efficiency and government commitments to the international forum for climate change has gained

momentum, a lot is to be done in order to change the status quo.


4. Gaps in existing demand assessment and forecasting methodologies

Over the last half decade, the Indian power sector has witnessed a few success stories and has undergone

dynamic changes. However, the road ahead is entailed with innumerable challenges that result from the existing

gaps between what is planned and what the sector has been able to deliver. Demand forecasting of power, thus,

has a significant role in trying to minimize these gaps.

The subject of forecasting has been in existence for decades. It involves accurate prediction of future power

demand (magnitude as well as geographical location specific) over different planning horizon. Demand

assessment is an essential prerequisite for planning of generation capacity addition and commensurate

transmission and distribution system required to meet the future electricity requirement of various sectors of the

economy. The type and location of power plants is largely dependent on the magnitude, spatial distribution as

well as the variation of electricity demand during the day, seasons and on a yearly basis. Reliable planning of

capacity addition for future is largely dependent on accurate assessment of future electricity demand.

Electricity demand forecasting is an essential exercise for every utility as it is forms the basis for the development

and optimization of power portfolio across various term time horizons and for planning the network infrastructure

to cater to the growth in demand as well as other requirements such as loss reduction and technology

upgradation. While definitions of time horizons vary according to application and agency undertaking the forecast,

broadly forecasting is undertaken in three time horizons viz. short term, medium term and long term. The following

diagram provides a brief overview of the periodicity and application of electricity demand forecasting:

Figure 21: Periodicity and application of demand forecasting

Errors in forecasting can lead to bad planning which may be costly. Too high forecasts lead to more power plants

and transmission resources than is required – high burden of cost of capital. While on the other hand, low


forecasts prevent optimum economic growth and may lead to the installation of many costly and expensive-to-

run generators (having short gestation period). These costs are ultimately borne by the consumers.

A glance at the projections made by the CEA in the 18th EPS for the 12th five-year plan viz. FY 2011-12 to FY

2016-17 indicate the year-on-year widening gap between forecast and actual. As indicated in the following

figures, the variation between the projected demand and the actual demand has shown an increasing trend.

Source: 18th EPS; LGBR

As can be seen from above graphs, the variation between projected demand, as per EPS projections and the

actual demand has been more than 20% overall.

The variation between what is projected and the actuals may be dependent on various factors like economic

conditions, methodology adopted, forecasting technique used, data reliability, usage of growth and other input

factors etc. Hence, going forward, there is a need to review and identify the same.

The details of the gaps identified are structured into three sections. First, the gaps are identified for the

methodologies that are commonly used to forecast power demand in India. Second, gaps due to economic

conditions have been shown and finally specific gaps pertaining to methodologies employed in draft NEP 2016,

19th EPS and draft Energy Policy 2017 are given.

4.1. General gaps identified based on review of commonly employed methodologies in India

Among the various methods that exist in the context of demand forecasting, three methods are most widely used

in case of electricity demand forecasting in India. These are – trend or time series, econometric and partial end

use method. The prevailing time horizons for demand forecasting are short term (upto 1 year), medium term (1

to 5 years) and long term (beyond 5 years) as per the requirements of the forecaster. The decision of selecting

a forecasting technique and methodology to be adopted depends on a lot of factors, few being the extent of data

availability, the applications of the forecasts, the accuracies intended and the level of effort one is willing to put.

Based on the review of the common methodologies and forecasting techniques used currently in India, the

following section provides a list of gaps which have been identified:

Granularity

The forecasts are majorly carried out at the Utility or State level. The granularity of the forecasts needs

to be increased by conducting demand forecasting at district level or below (like divisional/sub divisional

0%

5%

10%

15%

20%

25%

0

20

40

60

80

100

120

140

160

180

200

FY 11 FY 12 FY 13 FY 14 FY 15 FY 16

Peak Demand (GW)

EPS Actual Variation

-2%

0%

2%

4%

6%

8%

10%

12%

14%

0

200

400

600

800

1000

1200

1400

FY 11 FY 12 FY 13 FY 14 FY 15 FY 16

Energy Demand (BU)

EPS Actual Variation

Figure 22: Variation between the projected demand in 18th EPS and actual demand


level/ circle level) for better understanding of the behavior and trends and for higher accuracy of the

results.

Non-availability of accurate data

The reliability of any methodology depends on the availability and accuracy of data on parameters.

Availability of data is the biggest hindering factor for carrying out the forecasts. Few of the issues with

data are:

o Data is not at all available

o There is no process in place for data collection

o Data is not accessible

o Data is available but not in any standard formats thereby has limited usage

o Available data is unreliable

o Collected data requires extensive validation checks

With unavailability of required data, the methodologies developed are based on assumptions.

Improper assessment of parameters

The projections rely to a great extent on the estimations and assumptions of certain parameters.

Sometime the assumptions are not based on proper assessment of data but on unrealistic targets. The

use of such assumed parameters greatly affect the demand that is forecasted. The assumptions

considered for losses like T&D play a major role in estimating the energy required and should be

considered based on realistic basis rather than considering the targets.

Specific trends

The traditional forecasting methods such as time series and trend analysis may not take into account the

trends that are emerging or specific trends that may affect a particular category as such.

E.g. Addition of a single consumer in the Industrial category will have larger impact on the demand trends

when compared to a similar change in domestic category. The same may not be captured by a historical

growth trend. Hence bulk loads may have to be added. The effect of the changes in demand due to

schemes like PFA, improvement in power supply, energy efficiency and self-generation etc. may not be

captured by conventional techniques alone.

Lack of sensitivity

There is a lack of sensitivity of the commonly used load forecasting techniques towards changes in

weather parameters, customer mix, characteristics of an area, GDP growth etc. The techniques used

mostly provide straight line and point results.

Assumptions about factors

Forecasting is undertaken for a long term based on assumptions and assumptions change within the

forecasted period. The results of econometrical models rely on the assumptions and forecast of

independent variables. The higher the assumptions in a forecast, the higher the chances of variation with

the actual result.

Scenario models

The forecasting methods should be able to take care of the assumptions made about different inputs.

Different scenario models should be developed and analyzed to see how the forecast varies under

different scenarios.

Periodicity of forecasts

Currently demand forecasting is carried out after a period of 5 to 10 years. Over the period the

assumptions undergo changes. The exercise needs to be undertaken at shorter time period e.g. yearly,


to take into account changes in the factors affecting demand. There should also be a periodic review of

the forecasted demand and forecast should be updated periodically to maintain accuracy.

Non-reliability of inputs

Reliability of the inputs is paramount before using in any forecasting exercise for accurate modelling of

the behavior of electricity demand. Most of the time, the data and input variables provided are not reliable

which impacts the end result. Hence it is important that the data should be considered from reliable

sources and validated.

Seasonality and periodicity for forecast

The seasonality factors are not taken into account through the methodologies that are commonly used.

It is known that demand for electricity varies according to seasons. For example, the demand from the

domestic sector generally peaks when its summer and subsides during monsoon. During monsoon, the

power demand from the agricultural and domestic sector is lower while during summer and winter power

demand is higher as it is used for cooling and heating purposes.

Non-assessment of unmet/ Latent Demand

It is pertinent to note that if the demand supply gap is only on account of shortage of power, then

increased availability of power to that extent should eliminate the gap or shortage. However, it has also

been noted that there are scheduled and un-scheduled power cuts imposed by the Utilities on their

consumers not necessarily due to non-availability of adequate power but due to transmission &

distribution constraints, and poor financial health of utilities/DISCOMs making it difficult for them to

purchase power from available resources. The energy and peak deficit does not include unconnected

consumers. In the bid to supply reliable and quality power for each household, it is imperative to take into

consideration the latent demand assuming every person has to be provided at least fair minimum amount

of electricity. During forecast of demand, with improvement in economic conditions, the specific

consumption in future may not be captured by the historical growth rates. Hence, emphasis should be

laid on assessment of the same. The Standing Committee on Energy (2014-15) of the 16th Lok Sabha

had also recommended the Ministry of Power to issue necessary directions to the Central Electricity

Authority (CEA), for assessing latent demands of electricity in the country.

Non-linear relationships

Most of the commonly used techniques don’t incorporate non-linear relationships that may exist between

the dependent and independent variables. As many factors influence a demand and the impact of a

factor may vary over time, it is important that non-liner relationships are modelled. Such modelling will

require use of advanced methodologies and techniques. For example, temperature levels may affect

energy demand in a non-linear way. This non-linearity may also be due to geographical differences or

seasonal variations.

Inability to forecasting impact of new technologies

The forecasting of the future impact of new technologies is a challenge and hence assumptions and

scenarios are built. The inability is also due to unavailability of specific data. Hence, assumptions are

built into the models to take into account which increases the uncertainty of the forecast.

Combination of methods

No single method will provide an accurate forecast. Hence methodologies should be developed by

combining forecasting techniques with other inputs derived based on assumptions and experience.

E.g. CEA uses time series along with end use method to forecast power demand. Various techniques

can be applied and compared in order to minimize errors and to get reliable forecasts.


Use of advanced methodologies

The methodologies used commonly are not advanced and cannot incorporate behavior of several factors.

Also with large datasets, considerable time is required to prepare the data sets for using in conventional

methods. However, as the complexity increases a shift towards advanced techniques and hybrid models

will be required to arrive at more reliable forecasts. Few of the techniques which are used currently are:

o Artificial neural networks are computational methods that are non-parametric, non-linear, multivariate

and self-adaptive. These methods are gaining popularity due to their reliable predictions. These

methods can be applied when there are huge data sets and relations between the variables are

difficult to predict.

o Majority of the load forecasting techniques are based on point forecasting where a single expected

output variable is predicted. But with the changes expected in the demand-supply scenario, electricity

demand is more active and less predictable. As such, probabilistic load forecasting can be quite

useful. Such models can provide additional information on the variability and uncertainty of future

load estimates by providing a range of forecasts. Also due to the drawbacks existing in the

conventional methods (like lack of inclusion of seasonality elements) reliable forecasts are not easily

available in load forecasting practice. Use of probabilistic models provides a range of forecasts with

varying probabilities to get an idea of the possible future load demands

o In hybrid models, two or more advanced techniques can also be combined for forecasting. Such

hybridization could turn out to be very effective in case of load forecasting as different methods

possess different advantages in solving non-linear problems. A hybridization of these two techniques

can be used to incorporate the advantages of the two techniques and balance the deficiencies of

each technique and the combined approach delivers better reliable forecast.

4.2. Gaps in forecast due to impact of economic factors

Economic factors impact the demand of electricity in a country. Hence during demand forecasting, the economic

factors are reviewed and analyzed to ascertain its impact not only on the current demand but also on the

forecasted demand. The development of a forecast is dependent on the type of economic condition which has

been expected from now, to that period.

The gap in forecast occurs when there is a variation between the forecasted and actual economic condition.

Considering the example of 18th EPS which was published in December 2011, the projections were developed

based on the prevailing economic conditions at that time and with a view that demand will increase at a sustained

rate over the years. However, the actual growth of electrical energy requirement and peak electricity demand

was less as compared to the projections. The variation in the years in the shorter term was ~ 1% between the

forecast and the actual. However, over the period, the difference increased and there was greater variation

towards the terminal years.

The table below provides a comparison between the 18th EPS projections and the actual demand:

Table 27: Comparison of Energy Requirement between 18th EPS Projections and actuals

Year

18th EPS

projections

(MU)

Actual Energy

requirement (MU)

Difference

(%)

18th EPS

CAGR (%)

Actual CAGR (%)

2010-11 870,831 861,591 (1.06%) - -

2011-12 936,589 937,199 0.07% 7.55% 8.78%

2012-13 1007,694 995,557 (1.20%) 7.59% 6.23%

2013-14 1084,610 1002,257 (7.59%) 7.63% 0.67%


2014-15 1167,731 1068,943 (8.46%) 7.66% 6.65%

2015-16 1257,589 1114,408 (11.39%) 7.70% 4.25%

2016-17 1354,874 7.74%

2021-22 1904,861 - - - -

2026-27 2710,058 - - - -

It is worthwhile to analyze the probable causes which may have led to a difference between the forecasted and

the actual values.

4.2.1. Overall economic scenario during the period when 18th EPS was prepared

CEA had initiated the study for 18th EPS in August 2010 and the final report was published in December 2011.

The year FY 2009-10 was considered as the base year of the forecast. All the forecasts for 18th EPS were

developed based on the economic scenario that was prevalent during that period and with the assumption that

same shall continue in the forecast period.

CEA had finalized the forecasts for the 18th EPS with the following major assumption:

National GDP was assumed to grow at an average rate of 8 -10% till the end of 12th plan period (FY

2016-17) and around 7 - 9% beyond that period

All the other assumptions for the forecast were developed as per the prevailing economic conditions of

the year FY 2009-10 and FY 2010-11.

The assumption considered at that point in time may have been valid on the economic front. At the beginning of

the year FY 2009-10, the GDP growth was forecasted at 7.4%. Subsequently the same was revised to 8% on

account of improved performance from manufacturing and services sector. According to the ‘Quick estimates of

national income, consumption expenditure, saving and capital formation for 2009-10' released in the year 2011

by the Central Statistical Organization (CSO), the higher GDP growth rate during 2009-10 was achieved due to

high growth in manufacturing (8.8 %), financing, insurance, real estate as well as business services (9.2 %),

transport, storage and communication (15%), community, social and personal services (11.8%).

The economy continued its growth in the FY 2010-11 and the GDP registered a growth of 8.4%. The major growth

happened in the transport, storage and communication sector (14.7%) followed by financing, insurance, real

estate & business services (10.4%), trade, hotels & restaurants (9.0%) and construction (8.0%). The growth of

secondary sector was 7.2% and that of service sector at 9.3% during 2010-11.

In these two years, the major difference was the growth in the primary sector i.e. agriculture, forestry & fishing

that showed a high growth of 7.0% during FY 2010-11 as against 1.0% during the year FY 2009-10.

In view of the actual high growth achieved in the economy, the assumptions considered were also influenced by

it. The assumptions held true for the forecasts that were developed for the year FY 2011-12 and FY 2012-13 as

the difference between the forecast and the actual values for energy requirement for the two years are only 0.07%

and 1.20% respectively. However, for the year FY 2013-14, there was a wide variation between the forecasted

CAGR of 7.63% against the actual of 0.67%. The slump in the growth rate in FY 2013-14 lead to increased

deviation from the forecasted values.

In the subsequent section, the reasons for slowdown in the growth rate in the FY 2013-14 has been provided.


4.2.2. Reasons for slow demand growth in FY 2013-14

The year FY 2013-14 was marked by an overall slowdown of the economy due to domestic reasons like policy

paralysis, high inflation rate, stagnation of projects and lower manufacturing outputs etc. A host of other global

issues like high crude oil prices during that period also impacted in the overall slowdown of the economy in the

year FY 2013-14.

The reasons for slow demand growth in FY 2013-14 are listed below:

Downturn in the economic cycle: The slowdown in the year FY 2013-14 was primarily affected by the

downturn in business cycles. After achieving very high growth rates of ~ 8% in the previous 2-3 years,

there was an overall slowdown in the economy. India’s GDP during FY 2013-14 was registered at 4.7%.

Slowdown in the industrial segment: The major factor leading to slowdown was due to low growth in

the industrial sector. The industrial segment grew with an average of 0.4% per annum and there was

deceleration in major sectors like steel, automobile etc.

Mixed growth in infrastructure: The infrastructure sector showed a mixed trend. Sectors like power,

fertilizer, railways and civil aviation showed positive growth whereas sectors like coal, steel, cement,

refinery, crude oil and natural gas production witnessed lower or negative growth.

Decline in share of Agriculture: The share of agriculture in GDP also decreased from 15.2% in the 11th

plan to 13. 9% for the year FY 2013-14 indicating a further slowdown in the agriculture sector and a shift

toward non-agriculture activities.

Slowdown in the services sector: India’s growth was earlier attributed to the increase in share from

services sector. However in the year FY 2013-14, the sector grew at 6.8%, lower than FY 2012-13. The

sector recorded slow growth due to dismal performance primarily in trade, transport and storage

segment.

Decline in gross domestic savings (GDS): Gross domestic saving consists of savings of household

sector, private corporate sector and public sector. It is expressed as a percentage of the GDP. Since

FY 12, the trend has been declining and in the FY 2013-14, the GDS was lowest at 32.1% and it again

increased in the next year. Since GDS is expressed as a percentage of GDP, with lower GDP growth,

the net decrease has been substantial.

Decline in consumer durables segment: The segment decreased by 12.2% during FY2013-14 against

a growth of 2% achieved in the previous years. This was primarily due to high inflation and interest rates

which were prevalent during that time. Overall the demand for items in consumer segment was low.

Continued high inflation affected consumer spending: In FY2013-14, the average WPI was around

6% but across specific commodity groups which directly affect the consumer spending, the inflation was

still higher. Average quarterly inflation rate was 9.43% for food items and 10.14% for fuel and power. The

overall CPI was around 9.49% in FY 2013-14. The high inflation rate coupled with higher interest rates

affected the liquidity and spending power of the consumers.

The above mentioned economic factors have impacted the demand growth of electrical energy. Since, there is

no conclusive evidence to establish the extent of impact of each and how much each economic factor has played

a role in, the analysis is conjectural.

The next section deals with the specific gaps which have been identified on the methodologies employed in

forecasts by CEA.


4.3. Specific gaps on methodologies employed in forecasts by CEA

4.3.1. Draft National Electricity Plan, 2016

The draft NEP released in December 2016 provides the power demand projections at an all India level

developed using two methodologies

o One using historical growth rates

o Using regression technique

During the release of the Draft NEP 2016, the 19th EPS was under draft stage. Hence for undertaking

generation and transmission expansion planning studies, demand projections were developed.

The table below highlights the gaps identified:

Particular Methodology adopted Gap

State/UT as the base

level for forecast

The draft NEP 2016 considered the overall

historical consumption of each state/UT and

derived the overall growth factor.

The consumption at a lower level e.g. circle/

district and category wise in a State was not

considered.

Growth rate The growth has been considered based on

historical data for three periods and the rate

with the least standard deviations was

considered. The growth rate was fine-tuned

after considering the actual growth during FY

2014-15 and FY 2o15-16. Subsequently,

based on the growth rate adopted, the

forecast for each State/UT were developed.

In a developing country, the future growth

trajectory may not be consistent year on year

as it is dependent on the several factors like

economic performance, government policies

& programs and the extent of successful

implementation thereof etc. Hence,

consideration of a single growth rate to

forecast may not always provide an accurate

forecast.

Though historically, multiple growth rates

were looked into, during the forecast,

scenarios of growths have not been

developed.

The growth rates of shorter terms are ideally

given more weightages so that effect in the

nearer term are captured in the forecast.

Category wise growth rate was not

considered even though the demand and

growth of each category is different.

T&D Loss Overall T&D loss of the state was considered

to arrive at the total energy required for the

state. The actual T&D losses for the year FY

2013-14 has been taken as the base and

thereafter the T&D losses were assumed to

reduce during the period FY2014-15 to 2026-

27.

The draft NEP has not mentioned the

percentage reduction considered and the

methodology adopted to arrive at the

decreasing trend. The actual historical T&D

loss values for the state could have provided

a trend for a reduction in last five years. The

actual data of FY 2014-15 & FY 2015-16 was

not considered.

Effect of PFA in the

demand

The draft NEP mentions that the effect of

increase in electricity consumption on

Detailed methodologies and assumptions not

given.


account of PFA has been suitably

incorporated.

Owing to non-availability of reliable data,

method of estimation for future will be

assumption based on policy targets.

Effect of DSM, EE and

Conservation

Measures in the

forecast

The effect of the various measures were

considered in energy and peak demand at all

India level but detailed methodology is not

provided.

The effect of moderation, methodology

adopted and assumptions undertaken were

not highlighted. Data availability is a concern

for such estimations. Data for total savings

for the state will have to be derived based on

assumptions.

State specific savings in energy and peak

demand were not highlighted in the forecast.

This would have enabled better overall

planning of network infrastructure.

Base year of forecast FY 2013-14 has been considered as the

base year.

The reason for non-consideration of

FY 2015-16 as base year may be due to non-

availability of data from the states.

Load Factor All India load factor for FY 2013-14 was

considered to arrive at the all India peak.

State specific load factors not considered to

arrive at the peak.

Regression analysis to

forecast at an all India

level

The forecast of electrical energy

consumption on all-India level was also

worked out using regression technique with

dependent variable as energy consumption

and independent variables as 3rd and 4th

degree time variables.

Other independent variables like Population,

GDP, Per Capita Income, WPI, CPI and other

macro/micro economic factors not

considered to derive the regression equation.

The Regression method was also not carried

out at the state level using state specific

independent variables.

Measures like PFA, DSM, EE etc. are not

considered to refine the forecast developed

using Regression method.

Captive power

generation

The methodology assumes that there will be

a gradual transfer of energy consumption by

industries from captive power to utility grid

due to improvement in grid supply, thereby

increasing the overall demand in the grid.

The assumption is not backed by state

specific data. The correction in demand was

not carried out considering the situation at

individual states/UTs.

Roof Top solar Contribution of roof-top solar installation

towards reduction in electrical energy

requirement and peak demand has not been

considered.

The Govt. has set a target of 40GW for roof

top solar program by the year 2022. The

installation would affect the energy demand

initially due to self-generation and usage and

subsequently with storage, the peak demand

will also be reduced to some extent. However

the effect was not considered.

Effect of penetration of

Electric Vehicles and

storage devices

Not mentioned Impact of the expected demand from EV and

role of storage not considered in the

forecasts.


Base line correction of

data

Not mentioned The draft NEP doesn’t clearly state the

nature of historical data considered for the

state. The energy consumption data from a

state utility is mostly a restricted demand and

a correction of the baseline data should be

carried out before forecasting.

Methodology for correction of baseline data

for loss due to supply in unmetered

categories, theft etc. are not mentioned.

The forecast doesn’t highlight how with

up-lift of economic conditions, living

standards and infrastructure development,

the latent demand will be captured.

Scenarios considered

for Renewable

installed capacity

Three scenarios were considered upto 2022

i.e. 125GW, 150 GW and 175 GW.

The scenarios considered are not based on

historical installed capacity or growth but

based on top down assumptions.

Effect of bulk loads in

the forecast of

States/UT

Not mentioned The effect of bulk loads on the power

demand has not been mentioned. The

expected bulk load addition based on

Master/ Development plans of States are not

included.


4.3.2. 19th Electric Power Survey, 2017

The Volume-I of 19th EPS forecasts the energy and peak demand for the period FY 2016-17 to FY 2026-27 for

each category of each utility and state and subsequently aggregated to arrive at the regional and for all India

level. The report also provides the forecast for terminal years of FY 2030-31 and FY 2036-37. CEA has used the

PEUM methodology in Volume - I of the forecast and will release the forecasts using Econometric method

subsequently. In comparison to the 18th EPS wherein the base level for data and forecast was the State/UT, in

19th EPS the forecasts have been developed at the Utility level.

The table below highlights the potential gaps in the methodology adopted by CEA for 19th EPS:

Particular Methodology adopted Gap

Distribution utility as the

base level for forecast

The 19th EPS has considered the

overall historical consumption of each

Utility in the State/UTs and derived the

overall growth factor for each

category.

The consumption at a lower level e.g. circle/

district was not considered for the bottom-up

approach.

Base data considered for

forecast

The forecasts is based on the data

provided by the DISCOMs for the

state. Details of correction of baseline

data (if any) is not mentioned.

The data available from DISCOM is a restricted

data. Demand restricted due to outages

(planned/unplanned), feeder interruption, losses

due to thefts etc. should be considered to arrive

at the unrestricted base data. The methodology

for baseline correction is not clearly highlighted.

The major gap is the non-availability of data at

feeder/ circle level for the outages and if available,

it is not in the standard data required format for

usage.

Reliability of data available is a major concern.

Periodicity of data Annual electricity data from various

utilities considered to derive forecast.

Seasonal effects and trends of demand is not

considered. The reason may be due to non-

availability of month wise data at category levels

from all states.

T&D Loss The losses for each DISCOM has

been considered as per the trajectory

of loss reduction by respective

DISCOMs

The loss trajectory should be based on realistic

estimates from actual loss data conducted

through studies/ audit and not based on top down

targets.

Most DISCOM are not able to segregate

commercial losses from the T&D losses.

Determining the actual loss of a DISCOM is a

data intensive and is mostly constrained by

availability of reliable data at Sub-Division/ Circle

level.

AT&C Loss The AT&C loss reduction as agreed to

by the states who have participated in

the UDAY scheme of GoI has been

considered in the 19th EPS.

The actual loss figures should be considered

rather than agreed targets as historically there

has been variations.

Reliability of the figures quoted as actual loss

might be a concern.


Load and diversity factors Suitable assumptions considered The non-availability of data hinders the

determination of actual load factor for categories.

Hence assumption based approach is

considered.

DSM, Energy

Conservation, Efficiency

improvement programs,

Roof Top solar

programme

These initiatives have been factored in

the electricity demand forecast

exercise

Availability of data to measure efficiency gains

and units saved is a concern. Future estimations

and trajectories are assumption based and

subject to variation. Scenarios for efficiency gains

not developed.

PFA/ Make in India Initiatives would lead to growth in

demand. However no methodology

has been specified.

The estimations would be assumption based and

won’t be reliant on historical trends.

Electric Vehicles Suitable growth rate has been taken

into account in the demand forecast.

Considering optimistic scenario, if all

vehicles as per target are used, an

energy of 10-12 BU will be required

additionally at all India level. The

batteries will charge during day time

using solar generation and during off

peak hours at night. So appreciable

demand is not anticipated.

Estimation is assumption based. The acceptance

and penetration of EVs in future will be dependent

on policy push and initiatives initially. Scenarios

for the impact on demand not developed.

End Use Method End-use demand is affected not only

by the cost of energy but also by other

factors such as climatic conditions,

affordability and income of the

consumers, preference for end use

service etc. Similarly, demand for end-

use appliances depends on the

relative prices of the appliances, cost

of operation, availability of appliances,

etc. Projection of electrical energy

requirement for lift irrigation schemes,

railway traction and HT industries has

been done by end use method.

End use impact for other categories was not

considered.

Bulk load addition for domestic, commercial,

PWW categories etc. also not considered. Growth

rates based on only time as a dependent variable

may not capture the effect of other factors like

demand due to urbanization, impact of master/

development plans/ Smart cities etc.

The data for expected load in future and year of

commissioning of bulk loads are also not

available publicly or are uncertain.


4.4. IESS 2047 – analysis of the gap between demand and supply projections

NITI Aayog has developed the India Energy Security Scenarios (IESS), 2047 as an energy scenario development

tool. The guiding principle behind the development is to model the likely energy pathways that might happen in

the future and provide options to the user to customize and check the likely future scenarios.

The tool has been designed to develop scenarios between seven demand sectors and ten supply sources. Four

default pathways of likely energy scenarios have been pre-defined in the model. However the user has the option

of customizing the supply and demand sectors to develop unique scenarios.

In the IESS 2047, it has been highlighted that it is not a forecasting tool but its usage is for scenario development.

The model does take into account the known/ estimated energy resources potential of the country nor does it

factor in pessimistic/optimistic outlooks on policy, costs, economic growth and other assumptions. The data

highlighted are also indicative and not firm estimates.

Considering the above, the tool at best generates scenarios and provides a framework to balance the energy

numbers arising out of various combinations of demand and supply choices.

After incorporation of the demand and supply choices, the results in the form of energy demand and energy

supply required for a particular year are shown in the tool. What is observed after freezing such a scenario is

that, there is a mismatch between the energy demand and energy supply.

Broadly, the observed gap may be due to the following reasons given below. The reasons given are based on a

high level assessment that was carried out on the IESS2047 tool.

Varied conversion efficiency of the primary energy source

The extent of mismatch between supply and demand may be due to conversion efficiency of the primary

energy source selected by the user.

For e.g. for a particular demand, depending on the type of scenario selected, the share of a particular

supply source will be different in aggressive effort scenario than in case of a least effort scenario. In the

least effort scenario, the demand will be met more from coal while in case of aggressive scenario, solar

might have a greater share in meeting the same demand. Since coal and solar have different energy

conversion efficiencies, to meet the same demand, the supply required will also vary depending on the

type of fuel chosen which is again controlled by the scenario selected.

In the energy flow diagram, the same is captured under the head ‘losses’.

Energy lost due to T&D losses

During energy flow from supply to demand, there will be some amount of losses due to transmission or

distribution of energy. These are inherent losses which occur at every step until the energy reaches the

demand side.

In the energy flow diagram, the same is captured under the head ‘T&D losses’. The year wise T&D loss

numbers are provided in the excel model, however no details have been provided for the basis of the

trajectory assumptions considered.


5. Proposed alternate approach and methodology

5.1. Proposed hybrid method

The review of the existing methodologies highlighted that there are inherent gaps in existing methodologies which

needs to be addressed. Broadly, the gaps can be classified under the following:

Gaps in the approach and methodology

Gaps due to non-availability and unreliability of data

Gaps on assessment of input factors considered

Gaps in forecasting techniques used

In order to address the identified gaps and considering the anticipated impact of new influencers on the demand

in future, a hybrid method will be required which will involve use of a number of methods.

Every method has its merits and demerits. Every method

is dependent upon a set of input variables. Application of

a single method for predicting demand is not possible for

every consumer category (data limitations, use of too

many assumptions etc.) as nature of every consumer

category is different and with new influencers impacting

the categories differently, the behavior will also be

different. Thus for demand forecasting in the present

study, hybrid method will be used wherein the available

data is compiled and then on the basis of its availability,

decision on use of method will be undertaken.

The hybrid method will also rely on scenario development

wherein forecasts using different methods will also be

developed considering probable future scenarios. This will

ensure mapping of uncertainties and will result in a range

of forecast.

In spite of the use of hybrid method, there may still be challenges in addressing some of the gaps e.g. pertaining

to non-availability and unreliability of data. Such gaps will have to be addressed in the approach by considering

suitable assumptions.

The use of category specific forecasting method will also require that the approach to forecasting is also looked

into differently. To meet the objectives of the study and to develop an alternate methodology, a bottom up

approach will be more favorable.

5.2. Use of bottom up approach

Overall, the approaches to forecasting can be broadly categorized into bottom up and top down. In a bottom up

approach, forecast of individual units are summed up to arrive at the aggregate level forecast whereas in a top

down, forecast is disaggregated based on proportions and assumptions.

The following graphic highlights a comparison between the bottom up and top down approach to forecasting.

Hybrid

method

Trend analysis

Econometric method

Time SeriesNew Load additions

Scenario development


Figure 23: Comparison between bottom up and top down approaches

While both the approaches should be tested in order to derive the best results. However, considering the

requirements of the study, overall a bottom up approach shall be followed for undertaking the electricity demand

forecasting. To achieve higher granularity of forecasts, bottom up approach is the preferred approach in the

present context. It is proposed to undertake the forecast at the circle level of utility and then aggregate it up to

the higher level. The idea is to analyze the trends at the lower level so that better forecasting accuracy is achieved.

However, there is a risk of non-availability of requisite data and available data may not be reliable.

5.2.1. Proposed 18-step alternate methodology

The following figure summarizes the 18-step alternate methodology of electricity demand forecasting for the

present study.

Figure 24: 18 Step alternate demand forecasting methodology

1. Baseline creation -

correction of the baseline historical

data

2. Calculation of unserved/

unrestricted demand

3. Analysis of historical data to gather insights and understand

behavior

4. Selection of forecasting

methods for each forecast period

5. Use of trend method for short/

medium term

6. Use of time series method for the short/ medium

term

7. Identification & selection of independent

variables

8. Development of econometric

equations and forecast

9. Assessment of impact of Electric

vehicles

10. Assessment of specific

demand using end use methods

11. Assessment of bulk/ additional

demand

12. Assessment of latent demand

13. Assessment of impact of

energy efficiency

14. Development of forecast scenarios

15. Analysis and estimation of future loss trajectory

16. Analysis and estimation of of load factor and peak demand

17. Finalization of forecast results

18. Validation of results with traditional

methodologies


In the past, DISCOMs have been supplying power for restricted demand

conditions and managing the extra power requirements though load

curtailments etc. But going forward, the aim is to supply uninterrupted power.

DISCOMs are expected to serve 24x7 supply (unrestricted supply) to its

consumers (except for some category of consumers including agriculture etc.).

In India, many states have surplus status viz. supply/ availability outweighs the

demand, however in many places there are interruptions due to planned load

shedding, unplanned outages and due to failure of distribution infrastructure.

Thus, the forecasts should be based on a baseline data which is at the same wavelength as expected future data

i.e. using unrestricted baseline data for predicting our future unrestricted demand. The same will also ensure that

the historical demand data which is used for projecting the future demand is at the same base i.e. 24 hours

supply. The usage of correct base will ensure precision in forecasts.

The proposed methodology takes into account the development

of unrestricted demand data for all consumer categories using

bottom-up approach.

In the proposed approach, the supply hours will be considered as

per the data available from sample feeders to be shared by the

DISCOMs. The data will be extracted from feeder meters and

then supply hours for normal and block supply will be analyzed to

ascertain the current condition of supply. The basic premise for

this method lies in estimating the energy units to be supplied in

non-supply hours using actual data available for supplied units in

supply hours using straight-line method.

The curtailed energy for a certain category of consumer is

estimated using:

o Average hours of supply = [Y1]

o Average per day actual restricted supply units = [X1]

o Average daily un-restricted supply units [X2] = [(24 - Y1) * (X1 / Y1)] + X1

Accordingly, the unrestricted supply units for a region and for each category will be determined.

The unserved / unrestricted demand can be assessed using the supply hour

data available from sample feeders. Due to various factors like load shedding,

transformer failure and 33/11 kV feeder breakdown etc., there is interruption in

the power supply and is recorded by feeders. During the time of power

interruption, the demand is unserved. The supply hour data from sample feeders

will provide an indication of the quantum of unserved demand and can be used

to ascertain the unrestricted demand. Individual data for each type of failure is

not available and mostly feeder interruption counts are specified. However,

additional impact may be captured using suitable adjustment factors. Once

unserved units due to various responsible factors is assessed, the same will be

added up to the served units to get the unrestricted sales units.

Step 1:

Correction of the

baseline historical

data

Step 2:

Calculation of unserved

demand and

determination of

unrestricted demand from

baseline correction

Figure 25: Representation of a straight

line method


The foremost step before using any method is to analyze the historical data.

Understanding the dynamics of sales mix and behavior of LT and HT in future

is very important. The same impacts the load factor, profile, and also average &

peak demand of system. This factor is impacted with the migration/ growth of

industries or LT consumers including residential & commercial etc.

The analysis of the data also gives a guidance of the type of forecasting method

that can be used.

There are a number of forecasting methods (Econometric, Time series, Trend

analysis etc.) through which electricity demand can be forecasted. For

forecasting demand under various time horizons (short, medium and long),

combination of methods will be used for different forecast periods. Every method

has its merits and demerits. Every method is dependent upon a set of input

variables. The selection of the method would depend upon various factors like

availability of required data set, practical feasibility of the results obtained from

different methods, evaluation of the results obtained through goodness of curve

fit, stakeholder consultation etc.

In the present study, we propose to use the following four methods.

Post the analysis of the historical data, growth rates (year on year growth and

CAGR growth) will be calculated on the baseline corrected data of energy

consumption, consumer data etc. and analyzed. The process will be carried out

for each consumer category and will be repeated for all the regions. The growth

rates will provide a fair estimation of the trends of each category and region.

Post review of the rates, trend method will be applied for each category of

consumer. The application of trend method for specific categories may vary and

different approaches as per requirement will be developed. The model for trend

method will also have factors to incorporate effect of energy efficiency, effect of

technology advancement, latent demand etc. It is also planned to use different

CAGR rates for short, medium and long term.

The time series method will also be used to develop forecast for the short and

medium term with the baseline corrected energy demand data. For a better

accuracy, the method which considers trend and seasonality will be employed.

A case study highlights the experience of using the time series method in

electricity demand forecasting.

Step 3:

Analysis of historical data

– category wise to gather

insights and understand

behavior

Step 4:

Selection of forecasting

methods for each forecast

period

Step 5:

Use of trend method to

develop short/ medium

term forecast

Step 6:

Use of time series

method for short/ medium

term

Trend method

for short/ medium term

Time series method

for short/ medium term

Econometric method

for short, medium & long

term

End use method

for short, medium & long term


For undertaking the forecast using econometric method, identification of

independent variables in a crucial step. In the identification stage, a large

number of variables are looked into and tested. Following is a tentative list of

the independent variables which will be considered.

Table 28: Tentative list of independent variables

From a macroeconomic consideration, all the parameters mentioned above will

be assumed to influence electricity demand. For setting up the econometric

model, an initial functional form will be formed with electricity demand as the

dependent variable and all other parameters mentioned above as independent

Step 7:

Identification & selection

of independent variables

Independent Variables – Tentative list (subject to data availability)

Temperature Population

Humidity GDP or GSDP – Primary

Rainfall GDP or GSDP – Secondary

Consumer Price Index (CPI) GDP or GSDP – Tertiary

Wholesale Price Index (WPI) Per capita income

Rate of Inflation Electricity tariff

Number of Consumers Industry or Manufacturing indices

Use of time series forecasting technique to develop short term forecast for a private utility in India

In one of the recent assignments for a private utility, exponential smoothing technique was used to derive

monthly forecasts for the short term horizon. The graphic shows an illustration of the forecast results.

In the assignment, monthly sales data was used for previous ten years and the forecast was developed using

exponential smoothing technique. The results obtained for the short term were fairly accurate.

Figure 26: Case study: Time series method


variables. The correlations will be calculated for all consumer categories. Then

regression analysis will be conducted on the software - Statistical Package for

Social Sciences (SPSS) to determine the co-efficient of the independent

variables and test for significance of these variables. The variables which will

have a significance in the 95% confidence level will be chosen and the rest of

the insignificant variables will be dropped from the set of variables. Only those

variables, wherein, the p - value and t-stat are significant will be considered for

developing the equations. For significance, p-value should be less than 0.05.

Post the selection of the variables, the regression equations will be developed

with the significant variables and initial functional form to derive the final

coefficients.

After selection of the independent variables for each category, the final

econometric equations will be developed. As the regression equation is a

function of various independent variables, the dependent variable can be

established only if values of all independent variables are known. Hence to

estimate the future value of the dependent variable, the forecast of independent

variables are worked out using various approaches. Subsequently, electricity

demand forecast is worked out. The values obtained for each category is

aggregated to arrive at aggregate level forecasts.

Step 8:

Development of

econometric equations

and forecast

Use of econometric forecasting technique to determine electricity demand for a state utility in India

In one of the assignments for a state utility, econometric forecasting was used to derive forecasts for a

period of ten years. The graphic shows an illustration of relationships developed for each customer

category for a particular region in the state.

The significant variables obtained were carefully analyzed statistically and then final variables are selected.

The regression equations were developed post finalization of the significant variables.

Figure 27: Case study: use of econometric forecasting technique


The Government has set an ambitious target for an all-electric vehicle economy

in the country by 2030. The push for same started initially in the year 2013, when

NEMMP 2020 was launched to promote hybrid and electric vehicles in the

country. Subsequently FAME scheme was launched w.e.f 1st April, 2015 with

the objective to support hybrid/electric vehicles market development and

manufacturing eco-system. The scheme has four focus areas i.e. technology

development, demand creation, pilot projects and charging infrastructure.

The uptake of EVs will depend in large part on the adequate deployment of

electric vehicle supply equipment (EVSE) needed to recharge EVs. The systems

are still under planning and estimations have to be developed for the additional

power to be required to support these EVSEs. At present, there is no definite

data for the number of charging infrastructure available in the selected state nor

there a definite outlook of the same. Hence the estimations will be based on

scenarios and assumptions.

The end use method will be used to determine specific demand of certain

categories which cannot be captured from historical data or secondary research.

The approaches for end use method will vary depending on the target and the

end result. E.g. for determination load from agriculture pumps, an end use

method shall be used. The power demand for irrigation shall be worked out by

projecting the number of pumps in mid-year, the average capacity of each pump

and the average consumption per load or hours of operation with assumptions.

The demand for lift irrigation shall be based on the end use to be worked out

based on the programme of development of lift irrigation schemes indicated by

Distribution utilities/ State Authorities.

Open Access is another important factor which will be analyzed as it impacts

the demand in all time horizons (short, medium and long). The parameter will be

assessed considering the present and forecasted open access consumers, their

prevailing contracts and benefit analysis, likelihood of their returning back to

DISCOM system, regulated tariffs of DISCOM and expected increase in same,

policy changes etc.

Captive power generation is another aspect which will be looked into during the

forecasting study. The industries have been using captive power units to meet

their power requirements in the past. However with stringent environmental

norms, it is estimated that many captive power plants won’t be able to meet the

requirements and will have to be shut down. The resultant demand is expected

to progressively shift to the grid. Since data acquisition will be a challenge in this

regard, the estimation will involve assumptions.

Impact of additional demand will also be incorporated at this stage post the

development of the forecast to refine the results. Any additional load will be

added up to the demand arrived by the hybrid approach. The information on

such high loads shall be captured by consultations, primary and secondary

research, review of infrastructure schemes, housing schemes and master plans

etc. Depending on availability of the information on additional load, phasing will

be carried out and scenarios will also be developed for various factor which

impact the demand.

Step 9:

Assessment of demand

from electric vehicles

Step 10:

Assessment of specific

demand using end use

method

Step 11:

Assessment of bulk/

additional demand


Development of reasonable estimates for the latent demand in the system is an

essential pre-requisite to undertake an electricity demand forecast study as the

trend of historical consumption of electricity may or may not be a reliable

indicator of the future trend of electricity consumption. Latent demand is the

desire to consume a product or service but due to various barriers, the desire is

not met and the consumption is curtailed. In developing economies there are

various factors which may limit electricity consumption. These can range from

inadequate infrastructure such as network capacity constraints, negative effect

of income, high cost of supply, outdated technology etc. Considering the case

of a household with low income, consumption is based on priority of basic needs,

thereby impacting demand of other goods e.g. electricity. For an industry, the

electricity demand may be suppressed due to factors such as inadequate

network infrastructure, insufficient power availability with distribution licensee to

cater to the demand, high cost of supply, reliability of supply etc. To assess the

latent demand, different approaches will be attempted. One planned approach

will be using econometric method whereby the impact of latent demand due to

economic conditions can be captured. In an econometric forecast, the factors

will be checked for relationship with electricity demand. In case a strong

relationship is found, the same shall be considered. Another method can rely on

using suitable benchmarking of electricity consumption per consumer in other

countries/ regions wherein with that of target region to see the deviation. Such

methods will involve consideration of suitable assumptions. A more prudent

method will be to conduct primary survey across consumer categories in a target

region to understand the quantum of latent demand in the area and in

categories. Such a study will also provide a good base for future studies to

follow.

Step 12:

Assessment of latent

demand

Illustration of phasing of additional loads and scenario development

The graph below illustrates the forecasts of a private utility, developed for a ten year period considering

different scenarios.

Figure 28: Illustration of using additional loads in forecast


As per The World Bank estimate, in India the residential end use appliances,

agriculture/irrigation pumps, and municipal infrastructure are the top three areas

which will contribute to the energy efficiency potential. The Government has also

launched several programme and schemes pertaining to promotion of energy

efficiency in this regard. As the population grows and the demand for electricity

rises, there will be increased opportunity for energy efficiency to make an

impact. Same will be in the case of industries and buildings. Hence going ahead,

incorporation of the impact in the forecast is critical. It will also help in

ascertaining the behavior change due to efficiency and to estimate any net

savings if any. Research has suggested that the behavior of households are

very different from what is expected while designing the policies.

In order to capture the behavior and trend, multiple approaches have been

proposed. A primary survey has been proposed for the assignment to collect

relevant data. The survey will be designed to understand the impact and

influence of energy efficiency in households. Depending on the results of the

survey, suitable scenarios and assumptions shall be built and incorporated in

the forecast. The assessment will also rely on secondary literature in order to

consider the impact of energy efficiency.

The results may be forecasted using the best possible method and with all

necessary information like proposed infrastructure plans, weather variables,

economic & demographic variables etc., but there is always a probability that

the assumptions may not hold true. Hence, scenarios are developed to reduce

the uncertainties.

The forecast for the present study shall involve development of two scenarios

Forecast using baseline corrected data

Forecast after adjusting for the anticipated unserved demand in future

The scenarios have been proposed considering the aim the study which was to

undertake forecasts using baseline correction of historical data. The results of

the forecast in each scenarios will enable comparison with the results of the

existing methodologies wherein such baseline corrections are not undertaken.

Step 13:

Assessment of the

impact of energy

efficiency

Step 14:

Development of forecast

scenarios

Illustration of forecasting results post development of scenarios

Figure 29: Illustration of forecast results


One of the gap identified in estimation of loss trajectory is that the trajectories

are developed based on top down targets. Historically, it has been observed that

the targets are not met as per the defined timelines and are also periodically

revised. Due to which the overall forecasted results vary with that of the actual.

Hence it would be more prudent to develop the future loss trajectories based on

realistic estimates after analysis of the actual loss data.

The data pertaining to T&D losses may be available for a limited period and

mostly at state level. Also, such data also won’t have segregation of losses for

specific activities like theft, faulty reading etc. Such information may be available

only for select states wherein load flow studies have been carried out by the

Utilities in the past. Load flow studies have been previously carried out in states

of Orissa, Bihar and by some private utilities for their network. It helps in

identifying the losses at various levels and utilities can take their network

decisions based on the results of such reports. Before undertaking an exercise

of load forecasting, the DISCOMs should also undertake load flow studies in

their network on a periodic basis.

In the current assignment, the trajectory shall be developed based on in-depth

analysis of available data, trends observed and realistic assumptions will be

considered wherever required. Extensive stakeholder consultation shall be

involved before finalization of the future loss trajectory.

The following case study highlights the development of a future loss trajectory

for a private utility.

A detailed analysis will be carried out on historic load curves for the state

depending on availability of data to observe the loading patterns for peak,

average and base demand values. From this analysis, the expected load factors

for the forecast years will be computed using the historical data of energy and

demand. The load factor, defined as the ratio of the average load to the peak

Step 15:

Analysis and estimation

of future loss trajectory

Step 16:

Analysis and estimation

of load factor and peak

demand

Illustration of loss trajectory

The illustrated T&D

loss trajectory for the

utility was developed

based on past

behavior,

stakeholder

consultation and with

realistic

assumptions.

Figure 30: Case study: Illustration of a loss trajectory


load will provide an estimate of the degree of loading prevalent for each

consumer category. From the data, average load factors for each category will

be calculated. After forecasting the unrestricted demand, the average load factor

shall be used to determine the peak demand in the system.

The results obtained from all the methods will be analyzed and compared. The

exercise will be carried out category wise and post the analysis, the results from

the best fit model will be identified and selected. In some categories, a particular

method may not even produce the required forecast and will have to be dropped.

The forecast finalization will also involve consideration of results from bulk load,

specific demand, energy efficiency, latent demand etc. The finalization will be

for two scenarios as defined in the previous step. Overall based on insights from

historical data, after discussion with stakeholders and from experience of

forecasting, suitable judgement will have to be applied.

The final results will then be compared with available forecasts which have been

developed using other traditional methodologies for the same period. The

comparison will then be analyzed across common parameters and validated.

Step 17:

Finalization of forecast

results

Step 18:

Validation of results

with traditional

methodologies


6. Approach adopted for selected state

6.1. Rajasthan - Profile

Rajasthan is India's largest state by area (342,239 square kilometers (132,139 sq. mi) or 10.4% of India's total

area). Rajasthan is administratively divided into 7 divisions, 33 districts, comprising 295 panchayat samities,

9,894 village panchayats, and 43,264 inhabited villages.13

The state is primarily an agrarian state and ~75% of the state population (as per Census 2011) is rural population

and rest is urban population. The main industries are mineral based, agriculture based, and textile based.

The GSDP growth rate of the state has been consistent over the last five years. The state is expected to figure

amongst the growing states in India by capitalizing on the investor friendly policies launched by the state

government, excellent connectivity, and natural resources and with availability of labor in the state.

Figure 31: Rajasthan - GSDP

Figure 32: Sectoral contribution to GVA

Though the state has been primarily an agrarian economy and traditionally rich in mineral based industries, the

trend of sectoral contribution highlights that the contribution of services sector has been increasing at the expense

of industries and agriculture.

13 Government of Rajasthan, Economic Review 2016-17 including GSDP data

455,155 480,982 511,987 545,991 582,641

4.79%

5.67%

6.45% 6.64% 6.71%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

7.0%

8.0%

₹ 0

₹ 100,000

₹ 200,000

₹ 300,000

₹ 400,000

₹ 500,000

₹ 600,000

₹ 700,000

FY 2012-13 FY 2013-14 FY 2014-15 FY 2015-16 FY 2016-17

GS

DP

Gro

wth

ra

te (

%)

GS

DP

(In

IN

R C

r)

GSDP (2011-12 Constant prices) GSDP Growth Rate (2011-12 Constant prices)

0% 20% 40% 60% 80% 100%

FY 2016-17

FY 2015-16

FY 2014-15

FY 2013-14

FY 2012-13

Sectoral contribution of GVA at Constant (2011-12) prices

Agriculture Industry Services


6.2. Power sector in the state

The State of Rajasthan has been amongst the forerunners in initiating various reforms in the power sector which

can be gauged from the fact that the Government of Rajasthan brought about a slew of reforms in the late 90’s

and early 2000’s culminating in implementation of Power Sector Reforms Program (2000). The reforms in the

power sector aimed to facilitate and attract investments, bring about improvements in the efficiency of delivery

system and create an environment for growth for the overall benefit of the people of the state.

As part of the Power Sector Reforms Program, the State Government decided to restructure the erstwhile

Rajasthan State Electricity Board (RSEB) as a pre-cursor to unbundling on the functional lines. On March 21,

2000, Government of Rajasthan approved the provisional Financial Restructuring Plan (FRP) of the state power

sector and a provisional transfer scheme drafted. On July 19, 2000, Government of Rajasthan accomplished the

first major reform milestone of notifying “Rajasthan Power Sector Reforms Transfer Scheme 2000” and thereby

restructuring its vertically integrated Electricity Board (RSEB) to form five (5) successor companies.

Table 29: Present structure of power sector utilities

Function Company Objective

Generation

Rajasthan Rajya Vidyut Utpadan

Nigam Limited (RVUN)

Undertake the electricity generation

business of erstwhile RSEB

Transmission

Rajasthan Rajya Vidyut Prasaran

Nigam Limited (RVPN)

Undertake the electricity

transmission and bulk supply

business of erstwhile RSEB. In

addition, RVPN owns Rajasthan’s

capacity share in the shared power

stations of BBMB, Chambal

Complex and Satpura.

Distribution

Jaipur Vidyut Vitran Nigam Limited

(JVVNL)

Manage electricity distribution and

retail supply business in Alwar,

Bharatpur, Jaipur City, Jaipur

District, Dausa, Kota, Jhalawar, and

Sawaimadhopur Circles

Ajmer Vidyut Vitran Nigam Limited

(AVVNL)


retail supply business in Banswara,

Udaipur, Chittorgarh, Bhilwara,

Ajmer, Nagaur, Sikar and

Jhunjhunu Circles

Jodhpur Vidyut Vitran Nigam

Limited (JdVVNL)


retail supply business in

Sriganganagar, Hanumangarh,

Churu, Bikaner, Barmer, Jodhpur

City, Jodhpur District, and Pali

Circles

As a result of comprehensive reforms, the power sector in the state of Rajasthan has made significant progress

over the last decade in terms of increased generation capacity and strengthening of network infrastructure leading

to an improved power supply position. Most consumers are already being provided 21-22 hours of average daily

http://www.bing.com/images/search?q=grey+house+clip+art&qs=n&form=QBIR&pq=grey+house+clip+art&sc=0-0&sp=-1&sk=#view=detail&id=18E857FF7613FACAABCB878253706D62D406CE6F&selectedIndex=13

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supply14. The State is working towards connecting the remaining 20% households and provide 24X7 reliable

supply to all consumers in the state.

Figure 33: Electricity requirement and availability scenario in Rajasthan15

The requirement of electricity has been growing consistently over the years, but owing to supply constraints there

was deficit. Over the last 3-4 years, the deficit has narrowed down and the state is expected to be power surplus.

However, localized supply restrictions due to network and other issues prevail.

In terms of sales mix, agriculture is the dominant category and the share has increased over the last ten years.

Domestic and Commercial categories have shown marginal growth whereas demand from industries has

decreased substantially in the state.

Figure 34: Share of electricity demand across categories in the State

In the next 10-15 years, the demand in the state is expected to continue its growth trajectory due to the various

initiatives undertaken by the Central and State Government like providing connection to the unconnected

households, plan to provide 24x7 reliable power supply, strengthening of Transmission and Distribution network,

metering and loss reduction plans etc. to cater to the expected growth in demand of existing as well as

forthcoming consumers.

14 Rajasthan – Power For All document 15 LGBR – 2017-18 released by CEA

-4%

-2%

0%

2%

4%

6%

8%

50000

55000

60000

65000

70000

75000

80000

FY 12-13 FY 13-14 FY 14-15 FY 15-16 FY 16-17 FY 17-18 E

Su

rplu

s/ D

eficit in

%

En

erg

y (

MU

)

Requirement Availability Surplus/Gap

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

Domestic Commercial Agriculture Industry Other

Sh

are

in

th

e sa

les

mix

Major Categories

FY 2006-07 FY 2015-16


In the subsequent sections, the details of the implementation of the alternate demand forecasting methodology for the state of Rajasthan has been provided.

6.3. Use of a bottom up approach

6.3.1. Forecast at Circle level

As outlined in the alternate methodology which has been proposed for the current study, it was mentioned that

bottom up methodology will be used. To achieve higher granularity of forecasts, it was proposed to undertake

the forecast at the circle level of utility and then aggregate it up to the higher level. The idea is to analyze the

trends at the lowest level so that better forecasting accuracy can be achieved.

In view of the above, data pertaining to monthly sales, consumers etc. were collected at the Circle level from the

year FY 2006-07.

6.3.2. Consolidation of Circle level data

Rajasthan has three distribution utilities and many of the Circles have been carved out of existing bigger circles

over the past ten years for better management and operational reasons. During the course of data collection at

the Circle level, it was observed that data for many circles were not available since FY 2006-07 as many Circles

were formed post that.

Hence it was decided to undertake consolidation of Circles within the same distribution utility. The consolidation

of the Circles was carried out for the following reasons:

To ensure that minimum of 10 years of historical data across consumer categories is available

Historical data for new Circles was available for only 3-4 years prior to FY 2015-16

There was sudden drop in sales and consumers in the original circles as new circles were carved out.

This drop created inconsistency and would have impacted the forecast

In view of the above, the new Circles created post FY 2006-07 were added back to the original Circles so as to

arrive at the original number of Circles as was available in the year FY 2006-07. The details are given in the

following table:

Table 30: Consolidation of Circles for forecast

Sl. No. Circles Data availability since FY 2006-07 Circles kept for Forecast

JVVNL

1. Alwar Yes Yes

2. Bharatpur Yes Yes

3. Dausa Yes Yes

4. Jaipur city circle Yes Yes

5. Jaipur district circle Yes Yes

6. Jhalawar Yes Yes

7. Kota Yes Yes

8. Sawai Madhopur Yes Yes

9. Baran No. Added to Jhalawar

10. Bundi No. Added to Kota

11. Dholpur No. Added to Bharatpur

12. Karauli No. Added to Dausa


13. Tonk No. Added to Sawai Madhopur

AVVNL

1. Ajmer Yes Yes

2. Bhilwara Yes Yes

3. Nagaur Yes Yes

4. Udaipur Yes Yes

5. Rajsamand Yes Yes

6. Chittorgarh Yes Yes

7. Banswara Yes Yes

8. Jhunjhunu Yes Yes

9. Sikar Yes Yes

10. Pratapgarh No Added to Chittorgarh

11. Dungarpur No Added to Banswara

JdVVNL

1. Jodhpur city circle Yes Yes

2. Jodhpur district circle Yes Yes

3. Jalore Yes Yes

4. Pali Yes Yes

5. Barmer Yes Yes

6. Bikaner Yes Yes

7. Sri Ganganagar Yes Yes

8. Hanumangarh Yes Yes

9. Churu Yes Yes

10. Sirohi No Added to Pali

11. Jaisalmer No Added to Barmer

12. Bikaner district circle No Added to Bikaner

Post the consolidation, the forecast was planned to be undertaken considering 26 Circles of the State.

The consumer categories considered for the forecast are as per the categories defined by the Distribution utilities

in the State of Rajasthan. The historical data accessed for the ten year period is also segregated into the same

categories. For ease of forecast and based on category behavior, data for few categories have been consolidated

e.g. Industries, Public Water Works etc. after reviewing the historical data and based on the category behavior

exhibited in the past.

For the forecast, the category names as defined by the utilities have been retained and the results shall be

developed for the following categories

Domestic

Non-domestic

Public Street lights (PSL)


Agriculture (Metered, Nursery, Poultry)

Agriculture (Flat – unmetered)

Industries (Small and Medium) -

Industries (Large)

Public Water Works (PWW) – data for Small, Medium and Large added together

Mixed load

For each circle and utility, the forecast shall be developed for each of the above categories.

The forecast for Railway Traction shall be considered for the whole state as a whole and will be derived separately

and added to the demand of the state.


7. Baseline correction of historical data

The proposed methodology takes into account estimation of unserved electricity requirement and development

of a baseline corrected data for consumer categories using a bottom-up approach. The basic premise for this

method lies in estimating energy units to be supplied in non-supply hours using actual data available for supplied

units in supply hours using a straight-line extrapolation method.

Baseline correction of historical data is required for the following reasons:

Historical data of demand used for forecasts is energy units recorded at the consumer end and is a

‘restricted’ supply; Doesn’t reflect the entire consumer demand for the historic period

Due to failure of T&D infrastructure, planned load shedding and unplanned outages, demand of

consumers is not served even when the entire demand is existing in the system thereby leading to an

‘unserved demand’

Previously, the power sector in the country was in a deficit position viz. demand exceeding supply, while

in future it is anticipated that the availability of power will be surplus

Also, it has been envisaged to supply uninterrupted and reliable power to consumers in future.

Distribution utilities will be expected to serve 24 x 7 supply (unrestricted supply) to its consumers (except

for some category of consumers including agriculture etc.)

The forecasts should be based on a baseline data which is at the same wavelength as the expected future

electricity requirement i.e. using unrestricted baseline data for predicting future unrestricted electricity

requirement. The correction of the baseline will also ensure that the historical data which is used for projecting is

at the same base i.e. 24 hours supply. Usage of correct base will also ensure precision in forecasts.

In the proposed approach, the supply hours was considered as per the data available from sample feeders

accessed from the distribution utilities. The supply hours for normal and block supply was analyzed to ascertain

the current condition of supply across major categories.

Data collection was carried with support of the utility in the selected state. Discussions were held with utility to

understand the following:

Type of data available

Period for which required data is available

Format in which data is available

Process to be followed to access data

Feeder data for certain months was available as the same had been used by the DISCOMs for internal reporting.

The format in which the data was available was, however, not uniform across the DISCOMs. It was also

mentioned that data pertaining to interruption counts of feeders are mostly recorded for undertaking O&M

activities. However, that data cannot be directly converted into interruption in minutes as the duration of each

interruption varies.

Following figure highlights a format in which data was made available by the utility.


Figure 35: Circle wise feeder data accessed from utility

Data as available in the above format has been used to assess the unserved demand. Data, as received, is

checked for any inconsistency or for any missing variable. Exercise was repeated for daily and monthly data as

per the formats shown.

Post validation, it was observed that the supply hours were majorly categorized in three groups:

16-24 hours range

8 – 16 hours

Less than 8 hours

From the above observation in data, the gap in supply was determined for three consumer categories. The

assumptions considered for the three categories in terms of maximum supply hours be considered is as given

below:

Domestic 24 hour supply

Non-domestic 12 hour supply

Agriculture 8 hour supply

Since, clear tagging of feeders as Urban/ Rural is not available in the data, further categorization of Domestic,

Non Domestic (Commercial) and Agriculture for Urban/ Rural has not been carried out. Ideally, the raw data

should highlight the nature of the feeder (Urban/Rural) and to the category to which the feeder is being used to

supply.

For the other major categories namely Industries, Public Water Works, Public Street light etc., it has been assumed that the required supply has been provided as per demand.

The assumption has been considered due to following data constraints

In the accessed feeder data, segregation of industrial feeders at Circle level is not available


SLDC data records details of planned/ unplanned/ forced outages due to system constraints at state/

region level. In case of a particular system outage at state/ region level, it is difficult to estimate how

much of it has led to unserved energy for industry category in a circle.

From the daily and monthly supply hour data, average supply hours for each month and for each circle has been

determined.

Figure 36: Calculation of average monthly supply hours

Based on the monthly average supply hour determined for each feeder, the feeders were then categorized into

one of the three consumer categories. For e.g. as shown in figure above, the average monthly supply duration

for the first feeder is 7.43 hours. Hence, it has been categorized as an agriculture feeder and the gap in supply

has been considered w.r.t 8 hours of block supply.

Similarly, all the feeders in a circle were categorized into one of the three categories. Post the categorization, the

monthly average gap in supply for each time block for each circle was determined. The table below illustrates the

same.

Table 31: Monthly average supply gap of a sample circle

Category Time block Monthly avg. gap in supply

Domestic 24 hour 2.4 hours

Non domestic 12 hour 2.6 hours

Agriculture 8 hour 1.7 hours

The monthly average gap in supply hours was determined for all the months for which data is available. In the

present scenario, for the selected state the monthly feeder data was available for limited months for the year

2016-17.

Figure 37: Illustrative - circle wise average supply gap


The results as derived in the previous step for the particular period was considered for the same year i.e. FY

2016-17. This has been considered as the base year and the same has been extrapolated circle wise to arrive

at the supply gap for the previous ten year i.e. up to FY 2006-07.

The following provides an indicative supply disruption for the state, arrived by averaging the disruption in supply

hours for all sample circles.

Domestic 169 min average disruption; 21.2 hours average supply per day

Non-domestic 156 min average disruption; 9.4 hours average supply per day

Agriculture 143 min average disruption; 5.6 hours average supply per day

The supply interruption data given in the national power portal is for the total number of interruptions and total

duration of interruptions in a year. The same is not as per category wise and in several feeders the data is not

available. Hence direct comparison cannot be carried out unless category wise details are available.

The assumptions considered for the extrapolation are:

The gap in supply hours have been considered same for a particular year after averaging the

monthly data. Month wise variation have not been considered.

For the year previous to the current year, the gap in supply hours have been increased by 1%. The

same has been carried out for each circle and for the three categories which have been defined

earlier.

The assumption of 1% increase in YoY supply gap over the previous years has been considered

with the view that in the previous 10 years from FY 2006-07, the gap in supply hours has reduced

Since there is no definite data available for supply hours to validate, the assumption has been

considered based on realistic conditions that over the period of ten years, the improvement in gap

in supply hours has been very minimal. Hence consideration of a higher % (say 5%) would not

have been a realistic assumption in this case.

The assumption considered above could have been validated in case feeder level historical data

would have been available.

The following figure illustrates the assumptions stated above:

Table 32: Illustrative: Extrapolation of gap in supply hours highlighted for a sample circle

Category FY 13-14 FY 14-15 FY 15-16 FY 16-17

Domestic 2.47 2.45 2.42 2.40

Non domestic 2.68 2.65 2.63 2.60

Agriculture 1.75 1.73 1.72 1.70

The exercise was carried out for the historical data of all the circles. Post the extrapolation of the gap in supply

hours for each category is carried out for every circle, the next step is to determine the supply hours for each

category. The same is illustrated in the table below.

Table 33: Illustrative: Calculation of supply hours for historical data highlighted for a sample circle

Category Unit in Hours FY 14-15 FY 15-16 FY 16-17

Domestic Target supply 24.00 24.00 24.00

Gap in supply Y2 2.45 2.42 2.40


Actual supply Y1 21.55 21.58 21.60

Non domestic

Target supply 12.00 12.00 12.00

Gap in supply Y2 2.65 2.63 2.60

Actual supply Y1 9.35 9.37 9.40

Agriculture

Target supply 8.00 8.00 8.00

Gap in supply Y2 1.73 1.72 1.70

Actual supply Y1 6.27 6.28 6.30

* Y2 and Y1 - as defined in figure 1 of Section 3.1 of the report on Baseline correction

The above exercise was carried out for historical data for all the years.

The next step is to determine the unserved demand which could not be realized due to curtailment of supply. As

per the proposed methodology, the unserved demand was determined by a straight line approach using the gap

in supply hours calculated above and the restricted demand data available from the state utility.

The following section highlights the calculation for a sample circle done for the domestic category for the year FY

2014-15

Monthly average hours of supply per month estimated using the methodology described

above = [Y1] = 21.55 hours

Monthly average actual restricted supply units – Domestic category = [X1] = 148 MU

(sample value)

Monthly average hours of supply curtailed for Domestic supply = [Y2] = 2.45 hours

Monthly average un-served demand based on curtailed supply hours =[X2] = [(24* - Y1) *

(X1 / Y1)] = 16.82 MU

Average unrestricted demand after baseline correction

= [X1] + [X2]

= (148 + 16.82) MU = 164.82 MU

Based on the above calculation

Served restricted unit = 148 MU

Unserved units = 16.82 MU

Similarly, the unserved units was derived for each month of the previous ten years across categories and for

each circles.

The monthly unserved demand for each categories are then summed up to arrived at the yearly demand. The

following figure illustrates the calculation


Figure 38: Category wise monthly served and unserved demand

Figure 39: Category wise yearly served and unserved data

The year wise historical unserved demand determined for each category is added to the restricted demand to

arrive at the unrestricted demand. The exercise has been carried out category wise and for each circle.

The following figure highlights the overall demand data after baseline correction:

Figure 40: Historical data post baseline correction (Illustration)

The unrestricted demand determined above is the baseline corrected demand data which will then be used to

forecast the energy demand for each category.

The unrestricted demand (baseline corrected demand) arrived as above is the summation of the following

Served electricity (Restricted) – which is the electricity served by the utilities in the state to the consumers,

accessed from the electricity sales data of utilities

Unserved electricity – the portion of electricity demand which existed in the system but was not served

due to various reasons like load curtailment, infrastructure issues etc., derived from gap in supply hours

In the subsequent tables, the unrestricted electricity demand post baseline correction is provided. The baseline

correction has been carried out for each circles and then aggregated to arrive at the Distribution utility level.

In the subsequent sections, the steps of the methodology have been illustrated using a sample Circle. The Circle

of JPDC has been considered for the same. The results of the JPDC circle shall be used to show the three

methods that were used.

0

4000

8000

12000

16000

20000

FY 2006-07

FY 2007-08

FY 2008-09

FY 2009-10

FY 2010-11

FY 2011-12

FY 2012-13

FY 2013-14

FY 2014-15

FY 2015-16

FY 2016-17

Served energy Unserved energy


Table 34: JVVNL: Baseline corrected historical data

JVVNL MUs FY

2006-07 FY

2007-08 FY

2008-09 FY

2009-10 FY

2010-11 FY

2011-12 FY

2012-13 FY

2013-14 FY

2014-15 FY

2015-16

Domestic Served 1620 1911 2180 2658 3032 3142 3491 3761 4068 4418

Unserved 150 182 216 263 304 312 352 359 399 430

Total 1770 2093 2396 2921 3336 3454 3843 4121 4466 4848

Non-Domestic Served 647 780 835 898 996 1188 1550 1573 1805 1966

Unserved 172 205 219 233 258 304 391 398 445 480

Total 819 985 1054 1131 1254 1492 1941 1971 2249 2445

Public Street Light 55 61 75 79 92 110 129 143 168 175

Agriculture (M + N + P))

Served 884 1351 2062 2777 3288 4148 5135 4258 4741 5261

Unserved 389 585 876 1160 1349 1706 2047 1670 1828 1999

Total 1273 1936 2938 3936 4637 5854 7182 5928 6569 7259

Agriculture (F) Served 1017 1047 1137 1153 1051 783 724 581 530 517

Unserved 477 484 524 529 477 349 318 255 230 221

Total 1493 1532 1661 1682 1527 1132 1043 836 760 739

Industry (Small + Med)

645 723 760 796 898 904 941 996 1104 1036

Industry (L) 2071 2460 2747 3095 3520 3835 3721 3482 4294 3775

PWW (S + M + L) 288 310 328 346 355 387 416 426 474 529

Mixed Load 165 186 250 333 430 366 171 161 168 172

Restricted 7391 8829 10374 12136 13661 14863 16278 15381 17351 17850

Unserved 1188 1456 1835 2185 2388 2671 3108 2683 2901 3130

Total 8579 10286 12209 14321 16050 17535 19386 18063 20252 20980


Table 35: AVVNL: Baseline corrected historical data

AVVNL MUs FY

2006-07 FY

2007-08 FY

2008-09 FY

2009-10 FY

2010-11 FY

2011-12 FY

2012-13 FY

2013-14 FY

2014-15 FY

2015-16

Domestic Served 1069 1231 1421 1609 1893 2114 2410 2706 2925 3133

Unserved 153 175 199 223 259 286 320 355 380 402

Total 1223 1405 1620 1832 2152 2400 2730 3061 3305 3535


Unserved 93 110 118 121 131 150 204 230 250 275

Total 390 466 505 525 572 663 921 1045 1151 1280



Served 1079 1377 1623 1986 2217 2705 3464 3592 3545 3827

Unserved 471 592 688 830 914 1098 1372 1403 1365 1454

Total 1550 1969 2312 2816 3131 3803 4836 4994 4911 5281


Unserved 539 545 537 526 527 535 524 500 469 422

Total 1776 1813 1806 1786 1805 1851 1848 1782 1686 1534


570 639 680 756 798 860 908 954 1066 1089

Industry (L) 1626 1970 2011 1937 2465 2446 2487 2567 2630 2319

PWW (S + M + L) 261 294 327 338 354 383 387 426 435 478

Mixed Load 108 137 154 227 283 256 109 96 106 109

Restricted 6287 7311 7914 8558 9777 10646 11863 12500 12899 13154

Unserved 1256 1421 1543 1701 1831 2069 2420 2488 2464 2554

Total 7542 8732 9457 10259 11608 12714 14283 14987 15364 15707


Table 36: JdVVNL: Baseline corrected historical data

JdVVNL MUs FY

2006-07 FY

2007-08 FY

2008-09 FY

2009-10 FY

2010-11 FY

2011-12 FY

2012-13 FY

2013-14 FY

2014-15 FY

2015-16

Domestic Served 1071 1329 1414 1561 1811 2007 2255 2542 2793 3002

Unserved 154 188 198 216 248 272 299 334 363 385

Total 1225 1517 1612 1777 2059 2278 2555 2876 3156 3387


Unserved 98 122 124 125 135 154 222 236 251 270

Total 411 518 533 542 591 681 1002 1073 1156 1256



Served 1280 1849 2437 3456 4069 4810 6288 6443 7473 7964

Unserved 558 795 1033 1445 1678 1953 2490 2516 2878 3026

Total 1839 2645 3470 4901 5747 6763 8778 8959 10351 10990


Unserved 507 530 536 602 560 645 550 658 514 498

Total 1668 1763 1799 2043 1917 2234 1938 2343 1849 1808


466 535 550 576 638 694 781 803 850 846

Industry (L) 961 951 956 915 1077 1124 990 1077 1258 1183

PWW (S + M + L) 499 557 584 656 662 667 675 689 740 796

Mixed Load 286 320 359 457 533 488 335 324 350 351

Restricted 6078 7222 8052 9586 10721 12024 13615 14587 15845 16574

Unserved 1317 1636 1892 2389 2620 3024 3561 3744 4006 4179

Total 7394 8858 9943 11975 13341 15048 17176 18330 19851 20753


Table 37: JPDC (Sample Circle): Baseline corrected historical data

JPDC MUs FY

2006-07 FY

2007-08 FY

2008-09 FY

2009-10 FY

2010-11 FY

2011-12 FY

2012-13 FY

2013-14 FY

2014-15 FY

2015-16

Domestic Served 100 146 174 210 234 279 352 359 381 432

Unserved 14 20 24 28 31 37 45 46 48 53

Total 114 166 198 238 265 316 397 405 428 485


Unserved 13 17 19 24 25 36 50 54 62 65

Total 51 66 74 95 100 145 203 222 256 272



Served 197 316 464 641 652 930 1081 827 896 998

Unserved 100 158 229 312 312 438 495 374 399 438

Total 298 475 693 953 964 1368 1576 1201 1295 1435


Unserved 279 279 321 349 321 230 227 204 187 181

Total 827 836 971 1066 992 719 723 654 606 593


67 83 84 90 99 110 116 128 139 136

Industry (L) 187 239 328 398 506 634 597 559 704 584

PWW (S + M + L) 23 24 23 26 31 35 43 40 47 53

Mixed Load 10 12 17 24 33 34 10 8 7 9

Restricted 1173 1429 1799 2180 2305 2624 2852 2545 2794 2838

Unserved 406 475 593 713 690 742 818 677 695 737

Total 1579 1904 2391 2893 2995 3366 3670 3223 3489 3576


8. Implementation of alternate methodology

8.1. Analysis of historical data – category wise to gather insights and understand behavior

The category wise historical data of sales and consumers was analyzed for all the Circles on the following

aspects:

Category wise composition in a circle

Behavior of the category in the overall mix over the years

Growth rate over previous year

CAGR growth rates for the 9 periods

The analysis helped in understanding how the categories have contributed to the demand in a Circle and the

demand of the Utility. For e.g. in certain cases, domestic category in one circle had much less growth while in

another, the trend was different. The illustrations for the sample circle is highlighted below:

Figure 41: JPDC – Consumers: Category wise composition

Figure 42: JPDC - Consumers: Growth over previous year

Figure 43: JPDC Consumers: CAGR growth for 9 periods

Categories FY 2006-07 FY 2007-08 FY 2008-09 FY 2009-10 FY 2010-11 FY 2011-12 FY 2012-13 FY 2013-14 FY 2014-15 FY 2015-16

Domestic 61% 62% 64% 66% 68% 69% 70% 71% 72% 73%

Non-Domestic 8% 8% 8% 7% 7% 7% 7% 7% 7% 7%

Public Street Light 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

Agriculture (M + N + P)) 13% 14% 15% 16% 15% 15% 14% 14% 13% 13%

Agriculture (F) 16% 13% 10% 9% 8% 7% 6% 5% 5% 4%

Industry (Small + Med) 2% 2% 2% 1% 2% 1% 2% 2% 2% 2%

Industry (L) 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

PWW (S + M + L) 0% 0% 0% 0% 0% 1% 1% 1% 1% 1%

Mixed Load 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%


Figure 44: JPDC – Historical sales: Category wise composition

Figure 45: JPDC - Historical sales: Growth over previous year

Figure 46: JPDC - Historical sales: CAGR growth rates

From the figures above, observations as drawn are given below:

Share of domestic consumers has increased in the circle with corresponding increase in electricity

requirement

There has been a shift of consumers and demand from Agri (F) to Agri (M)

In the Industrial category, there has been growth in new consumers over the years, however demand in

the last 3-4 years has not picked up specifically in Industries (L) category

Similarly, specific trends were observed for each of the 26 circles across categories and this helped in

understanding the individual behavior. The trends as observed were used in undertaking the forecasts.


8.2. Selection of forecasting methods for each forecast period

There are a number of forecasting methods (Econometric, Time series, Trend analysis etc.) through which

electricity demand could be forecasted. For forecasting demand under various time horizons (short, medium and

long), combination of methods has been used for different forecast periods. Every method has its merits and

demerits. Every method is dependent upon a set of input variables.

After analysis of the historical data, the four methods which were used for the forecast period are given as below:

Table 38: Selection of forecasting methods

Sl. No. Method Periodicity Forecast level Data level

1. Trend method Short/ Medium/ Long Category wise in each

circle Yearly data

2. Time series method –

Holt Winters method Short/ Medium/ Long

Category wise in each

circle Monthly data

3. Econometric method Short/ Medium/ Long Category wise in each

circle Yearly data

4. End use method Medium/Long Specific categories Yearly data

As per the scope of work of this assignment, the short, medium and long terms requirements have been defined

as under:

Short term 5 years FY 2016-17 to FY 2020-21

Medium term 5 years FY 2021-22 to FY 2025-26

Long term 6 years FY 2026-27 to FY 2031-32

To start with the forecast, periodicity restrictions on the methods have not been imposed.

8.3. Use of trend method

Post the analysis of the historical data and after determination of the growth rates (year on year growth and

CAGR growth) for energy consumption, consumer data, the forecast model using trend method was developed.

The process was carried out for each consumer category and repeated for all the circles.

The application of trend method for specific categories is varied and different approaches and factors have been

used to develop the forecast.

Table 39: Details of category wise approach to forecasting

Sl. No Categories Method used Additional factors

1. Domestic Number of consumers x

Consumption per consumer

Growth rate of consumers

Growth of consumption per consumer

Manual option to enter above rates

Latent/ Lifestyle

Energy Efficiency

2. Non- domestic Number of consumers x

Consumption per consumer Growth rate of consumers




Energy Efficiency

3. Public Street light Number of consumers x




Energy Efficiency

4. Agriculture (MNP) Number of consumers x




Energy Efficiency

5. Agriculture (F) Number of consumers x




Energy Efficiency

6. Industries

(Small + Medium)

Sales x Historical growth rate Manual option to enter growth rate

Energy Efficiency

7. Industries (Large) Sales x Historical growth rate Manual option to enter growth rate

Energy Efficiency

8. Public Water Works

(PWW)

Sales x Historical growth rate Manual option to enter growth rate

Energy Efficiency

9. Mixed load Sales x Historical growth rate Manual option to enter growth rate

Energy Efficiency

10. Railways Forecast carried out independent of Distribution utilities

The approach as defined above was used separately for the three forecast terms i.e. separate trends were used

each of the short, medium and term terms to arrive at the forecast.

In summary, the method was used in:

3 Distribution utilities

26 Circles

9 consumer categories

3 forecast horizons – separate trends for Short, Medium and Long Terms

Therefore 702 combinations of individual forecast were developed using the trend method at the lowest forecast

level and then aggregated to arrive at the Circle, Distribution utility and State level forecast at unrestricted level.

The following table highlights the base forecasts arrived for the sample Circle – JPDC.


Table 40: Base forecast (Unrestricted) developed for JPDC circle using Trend method

JPDC (MUs) FY 17 FY 18 FY 19 FY 20 FY 21 FY 22 FY 23 FY 24 FY 25 FY 26 FY 27 FY 28 FY 29 FY 30 FY 31 FY 32

Domestic 522 562 604 648 695 748 804 863 925 990 1068 1151 1237 1328 1423 1521

Non-Domestic 293 316 340 366 393 423 454 488 523 561 601 643 688 735 784 836

Public Street Light 9 9 10 10 10 10 10 10 10 10 10 11 11 11 11 11


1523 1615 1712 1813 1920 2005 2094 2185 2278 2373 2470 2569 2670 2772 2876 2980

Agriculture (F) 508 443 386 337 293 232 183 144 114 90 62 42 29 20 14 9


142 148 154 161 167 170 173 176 178 181 183 186 188 190 192 193

Industry (L) 617 651 686 723 762 774 786 798 809 819 830 839 848 857 864 871

PWW (S + M + L) 57 60 64 68 72 77 81 86 91 97 102 108 114 120 126 133

Mixed Load 9 10 11 11 12 13 14 14 15 16 17 18 19 20 22 23

3680 3814 3967 4138 4325 4453 4600 4764 4944 5137 5344 5567 5804 6053 6311 6577

The base forecast for the Circle is excluding the following

Demand from Railways

Additional demand due to planned agriculture and domestic connections

Additional demand due to other factors like new infrastructure projects

Open access and captive

Electric vehicles

Similarly, the forecasts were worked out for all the other circles.


8.4. Use of Holts-Winter’s multiplicative and additive exponential smoothing

Exponential smoothing is a technique that is usually applied to time series data, either to produce smoothed data

for presentation, or to make forecasts. Whereas in the simple moving average method the past observations are

weighted equally, exponential smoothing assigns exponentially decreasing weights over time. In other words,

recent observations are given relatively more weightage in forecasting method than the older observations.

Holts-Winter’s multiplicative and additive exponential smoothing is one of the commonly used techniques used

for forecasting. This model extends Holt’s two parameter model by including a third smoothing operation to adjust

for the seasonality. The equation assumes that the trend is additive and that the seasonal influence is

multiplicative. This method models trend, seasonality and randomness using as efficient exponential smoothing

process. The figure below highlights how the method decomposes a historical data into the different components.

The forecast using his method has been developed using R software.

Figure 47: Decomposition of historical data by Holt-Winter's method

After the decomposition, the method models the historical data as shown below:

Figure 48: Modeling of historical data using Holt-Winter's method

Post the modelling, the monthly forecasts of electricity are developed for each category. The following table

highlights the base forecasts arrived for the sample Circle – JPDC.


Table 41: Base forecast (Unrestricted) developed for JPDC circle using Holt-Winter's method


Domestic 461 511 561 610 660 710 760 809 859 909 958 1008 1058 1108 1157 1207

Non-Domestic 275 295 314 334 353 373 392 412 431 450 470 489 509 528 548 567

Public Street Light

8 9 9 10 10 11 11 12 13 13 14 14 15 15 16 16


1781 1975 2169 2363 2557 2751 2945 3139 3333 3527 3721 3915 4109 4303 4497 4691

Agriculture (F) 252 39 0 0 0 0 0 0 0 0 0 0 0 0 0 0


165 178 194 211 229 249 269 290 312 335 359 383 408 434 460 487

Industry (L) 614 664 714 764 814 864 914 964 1014 1064 1114 1164 1214 1264 1314 1364

PWW (S + M + L) 58 63 67 72 76 81 85 89 94 98 103 107 112 116 121 125

Mixed Load 11 13 15 17 19 21 23 25 28 30 32 34 36 38 40 42

3626 3747 4044 4381 4720 5059 5400 5741 6083 6426 6770 7115 7460 7806 8153 8500

The base forecast for the Circle is excluding the following

Railway demand




Electric vehicles



8.5. Identification of Independent variables

For undertaking the forecast using econometric method, identification of independent variables is a crucial step.

In this stage, a large number of variables are looked into and tested. Since econometric method has been planned

to be used for all the categories, hence a broad range of major independent variables were shortlisted so that

impact on electricity can be found out.

Availability of historical data of independent variables is a critical aspect in selection. Also it has to be ensured

that the value of the variables are kept at the same base years for uniformity. For e.g. GSDP data is available in

base of 2004-05 and also for 2011-12. Since 10 years of historical data is being used from FY 2006-07, the data

for the relevant base years have been selected. The historical data of independent variables have been taken

from sources available in public domain. Following is the list of the independent variables which are considered.

Table 42: List of independent variables considered for econometric method

Sl. No Independent variables Data level

1. Population State level

2. Gross GDP State level, Constant prices at 2004-05

3. Per Capita State level, Constant prices at 2004-05

4. Wholesale price index State level (Base Year 1999-2000=100)

5. Consumer price index State level (Base Year 2001=100)

6. IIP General State level at Base year 2004-05

7. IIP Electricity State level at Base year 2004-05

8. IIP Manufacturing State level at Base year 2004-05

9. Index of Agriculture production State level

10. Area under crops State level

11. Area under irrigation State level

12. Category wise tariff State level, 9 categories

13. Number of consumers Category wise

Data sources: Economic review published by Govt. of Rajasthan; Population – Census data

8.6. Development of econometric equations and forecast

The Econometric analysis was carried out across 26 circles over 9 consumer categories with the shortlisted

independent variables via regression methodology in SPSS software.

From a macroeconomic consideration, all the independent variables mentioned have been assumed to influence

electricity demand. Certain category specific exclusions have been also undertaken. For e.g. the independent

variables for Agriculture were not included while developing the equations of other categories. For setting up the

econometric model, an initial functional form has been formed with electricity demand of that particular category

as the dependent variable and the selected intendent variables as independent variables. Then regression

analysis was conducted on the SPSS software to determine the co-efficient of the independent variables and to

test for significance of these variables. The equations were checked initially using the R sq. and adjusted R sq.

values. Those equations wherein the values are above 95% have been shortlisted. Also it has been checked that

even with high coefficient of determination, independent variables which were not having impact are dropped.


The equation and the variables used in the equation were selected after analyzing the

p – value and t-statistic in the regression results. Post selection of variables, regression equations were

developed with significant variables and functional form of equations were derived with the coefficients.

In many of the cases, equations which were not meeting test criteria were dropped. For few categories, no

relations could be formed. In categories wherein no equations were formed, the forecast developed using other

methods have been used in place.

After selection of the independent variables for each category, the final econometric equations were developed.

As the regression equation is a function of various independent variables, the dependent variable can be

established only if values of all independent variables are known. Hence to estimate the future value of the

dependent variable, the forecast of independent variables are worked out using trend method. Subsequently,

electricity demand forecast was worked out for each category and circle. The values obtained for each category

was then aggregated to arrive at aggregate level forecasts.

In the following table, the shortlisted independent variables and the equation developed for the sample circle is

shown

Table 43: Econometric equations developed for the Sample circle JPDC

Sl. No. Category Independent variables Equation

1. Domestic WPI, Domestic tariff -186.599 + 1.503 WPI +41.389 DomTariff

2. Non-Domestic Population, WPI, Number of

consumers

669.889 – 1.803E^-5 Population + 1.671 WPI

+ 0.012 Consumers

3. Public Street Light GSDP, Consumers - 1.514 + 2.032E^-5 GSDP + 0.06 Consumers

4. Agriculture (M + N + P)) GSDP, Tariff, IAP index, Area

under Crops

- 691.657 + 0.016 GSDP – 279.251 Tariff –

2.613 IAP – 3.043 E^-5 AreaCrops

5. Agriculture (F) No relation -

6. Industry (Small + Med) Population -310.072 + 5.989E^-6 Population

7. Industry (L) No relation -

8. PWW (S + M + L) GSDP, WPI, CPI, Consumers,

IIP General

15.363 + 0.001 GSDP + 0.175 WPI – 0.486

CPI + 0.005 Consumers – 0.336 IIP Gen

9. Mixed Load No relation -

Similar equations were developed for the categories in other circles also and forecasts were developed for the

circles. The forecast developed for JPDC circle is given in the following table:


Table 44: Base forecast (Unrestricted) developed for JPDC circle using Econometric method


Domestic 534 590 652 719 793 873 962 1059 1165 1282 1411 1553 1709 1882 2072 2282

Non-Domestic 321 359 400 445 495 548 607 671 740 815 897 985 1080 1184 1295 1416

Public Street Light

9 9 10 12 13 14 15 16 17 18 20 22 24 26 28 31


1461 1592 1724 1854 1981 2103 2219 2326 2421 2503 2566 2607 2621 2603 2546 2444

Agriculture (F) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


151 160 169 178 188 198 208 218 228 238 249 260 271 283 294 306

Industry (L) 614 664 714 764 814 864 914 964 1014 1064 1114 1164 1214 1264 1314 1364

PWW (S + M + L) 58 63 67 72 76 81 85 89 94 98 103 107 112 116 121 125

Mixed Load 11 13 15 17 19 21 23 25 28 30 32 34 36 38 40 42

3159 3451 3752 4061 4378 4702 5032 5368 5707 6049 6391 6731 7067 7394 7710 8009

The base forecast for the Circle is excluding

Railway demand




Electric vehicles



8.7. Impact of Electric Vehicles

The Government of India has set an ambitious target for an all-electric vehicle economy in the country by 2030.

The first push for the same started in the year 2013, when NEMMP 2020 was launched to promote hybrid and

electric vehicles in the country. Subsequently FAME scheme was launched w.e.f 1st April, 2015 with the objective

to support hybrid/electric vehicles market development and manufacturing eco-system. The scheme has four

focus areas i.e. technology development, demand creation, pilot projects and charging infrastructure.

The uptake of EVs will depend majorly on the adequate deployment of electric vehicle supply equipment (EVSE)

needed to recharge EVs. The systems are still under planning stage and estimations have to be developed for

the additional power which will be required to support these EVSEs. At present, there is no definite data for the

number of charging infrastructure available in the selected state nor there a definite outlook of the same. Hence

the estimation of additional power demand have been worked out based on assumptions and by taking reference

to available EV vehicle data.

The methodology used to forecast electricity requirement for EVs for the State of Rajasthan is as given below:

Historical data of all types of vehicles registered in Rajasthan was considered upto FY 2016-17

From FY 2017-18, for each vehicle types, conversion factors were used to estimate the number of EVs

that might be sold in the state. The premise is that EVs initial sales will start from consumers who will

shift from conventional fuel to EVs

The growth phase of EVs was divided into three phases and different overall sales growth rates were

assumed.

Phases Period Nature YoY growth rate

Phase I FY 2019-23 Initiation phase 10%

Phase II FY 2024-28 Growth phase 15%

Phase II FY 2029-32 Mature phase 20%

To compare the various vehicles types and to determine the motor capacities to be used in the vehicles,

data from currently available EVs were considered. Details are as given below:

Vehicle class Reference vehicle Motor Capacity

Motorized Rickshaws Mahindra eAlpha Mini 10kWh

Two Wheelers Hero Electric bike 1kWh

Auto Rickshaws Mahindra eAlpha Mini 10kWh

Tempo carrying goods - 2 x 10kWh

Tempo carrying passengers - 2 x 10kWh

Car Mahindra eVerito 30kWh

Taxi Mahindra eVerito 30kWh

Buses and Mini Buses Ashok Leyland Circuit 100kWh

* Trucks, Trailers, Jeeps, Tractors were not considered


For each of the vehicle types, average hours of charging were considered based on normal operation.

The methodology also considered the following assumptions

o Vehicle life considered for 15 years

o No EVs will retire during the forecast period

o The motor capacities have been assumed as constant during the period

o The phases of growth has been defined considering the maturity levels of EVs over the years

o It has been assumed that there won’t be any infrastructure lag from the supply side and the

demand will maintain the growth rates

Based on the above, the additional demand from EVs for the complete state has been worked out as given below:

Table 45: Additional electricity requirement (MUs) due to EVs in Rajasthan

FY18 FY19 FY20 FY21 FY22 FY23 FY24 FY25 FY26 FY27 FY28 FY29 FY30 FY31 FY32

44 92 145 204 268 338 420 513 620 744 886 1057 1261 1507 1801

8.8. Assessment of specific demand using end use method

The end use method have been used to determine specific additional demand of certain categories which could

not be captured in the base forecast developed using historical demand. The approaches for end use method

are varied depending on the target and the end result. In the following sub-sections, the demand assessed for

different categories are given.

8.8.1. Railways

The Electricity Act, 2003 has provided deemed distribution licensee status to Railways. In November’ 15, CERC,

in its order against a petition filed by the Railways had clarified that the Railways are authorized entity under the

Railway Act to undertake transmission and distribution, in connection with its working and Railway is a deemed

Licensee under Electricity Act.

For the state of Rajasthan, the demand from Railway traction was historically included as a separate category in

the sales of distribution utilities until the year FY 2015-16. Due to deemed licensee status, the demand is no

longer considered under the utilities. In the present forecast, the same has been incorporated separately as an

additional demand within the overall demand of the state. To forecast the traction demand in the state, the

monthly historical data for traction was analyzed and forecasted using time series methods to arrive at the

forecast of the upcoming years.

The following table provides the yearly demand assessed for Railway.

Table 46: Additional electricity requirement (MUs) from Railway traction

FY17 FY18 FY19 FY20 FY21 FY22 FY23 FY24 FY25 FY26 FY27 FY28 FY29 FY30 FY31 FY32

433 451 470 488 507 526 544 563 581 600 618 637 656 674 693 711

8.8.2. Domestic: Power For All (PFA) and Saubhagya scheme

The PFA scheme is expected to improve the entire value chain of the power sector and ensure reliable and

uninterrupted electricity supply to all households, industries and commercial establishments. The target was to

provide access to electricity to all un-connected consumers by FY 2018-19 and then provide reliable and


continuous power supply by the year FY2021-22. Power supply to agriculture and unconnected households is

also supposed to increase within this projected time frame.

In continuation of the above, Government launched the Saubhagya scheme which is targeted more towards

ensuring last mile connectivity in urban and rural areas and to release electricity connections to un-electrified

households in the country. With such changes happening, the overall demand and category wise consumption

is expected to increase.

As per available information, ~78% of the ~ 91.57 lakh domestic households in the state are connected and are

being provided electricity and the balance ~ 19.80 lakh households are to be connected as per the scheme. The

DISCOMs target is to connect 6 lakh household every year.

In the projections developed for the base forecast for the domestic category, because of the growth rate

considered, currently around 6 lakh consumers are being added in the year FY 18, FY 19 and FY 20 and hence

the demand has already be taken care of. No additional requirement has been added at this point.

8.8.3. Agriculture category

The Government of Rajasthan had launched a scheme in the year 2015 to provide 40,000 new agriculture

connections every year. In the base forecast, the yearly additions is already considering adding more than 40,000

new connections every year. Hence, no additional requirement is being added in the forecast.

8.8.4. Open Access and Captive

Open Access (OA) transactions in Rajasthan are largely categorized by majorly short term open access

consumers/ transactions and minimal medium/long term open access consumers and transactions. Short term

open access energy demand of the consumer is based on market trends i.e. the consumers will purchase energy

from exchange if the price prevailing at the exchanges is favorable to them as compared to the energy charges

recovered by the DISCOM from the industrial consumer. The switching by consumers between open access

power and DISCOM power is very dynamic and difficult to predict.

Over the past years the DISCOM observed stagnant or negative growth in consumption from Industrial categories

(Small/Medium/Large) largely due to increasing trend of short term Open Access transactions. With the

consumption going down from a major category, the stranded capacity of the DISCOMs increased leading to

further worsening of the already precarious financial position of the DISCOMs.

In 2016 the Rajasthan Electricity Regulatory Commission (RERC) notified the “Rajasthan Electricity Regulatory

Commission (Terms and Conditions for Open Access) Regulations, 2016” with the following main provisions:

Mandatory requirement to give a schedule for 24 hours

Requirement of uniform schedule for at least 8 hours

Not more than 25% variation in schedule during the entire day

Restricting deemed DISCOM drawl based on schedule provided by OA consumer on day ahead basis

These provisions resulted in reducing of the frequency of switching by the short term OA consumers. Moreover,

the regulations also ensured that consumers who used to declare fictitious demand of 1 MVA and above and opt

for Open Access are not able to do so anymore. Further the regulations have mandated that the open access

consumers maintain a contract demand equivalent to the quantum of power being procured from various sources

(including open access and DISCOM power).

RERC also came out with an Order on determination of Additional surcharge and Cross subsidy surcharge in

2016. The following were the rates determined by the RERC:

Additional surcharge of Rs 0.80 per unit

Voltage wise Cross subsidy surcharge for industrial consumer:

o 11 KV: Rs. 0.83 per unit


o 33 KV: Rs. 1.39 per unit

o 132 KV: Rs 1.63 per unit

With additional surcharge and cross subsidy surcharge determination by RERC, the DISCOMs observed a

reversal in the trend of Open access transactions in the State and this resulted in further reduction in the quantum

of power procured from open access. Upon analyzing the recent trends in DISCOM sales from industrial category,

it is observed that the sales in the first 6 months of FY 2017-18 increased by 15-20% as compared to the last

6 months of FY 2016-17. Similarly in case of open access transactions, it was observed that the power purchased

from open access and captive decreased by ~70% in Rajasthan in the month of November 2017 as compared

to November 2016.

Due to the above highlighted factors, it is observed that short term open access forecasting would be very

dynamic and is therefore is a challenge to forecast. Thus the quantum of Open Access power has been assessed

based on the following approach:

Combined data for open access and captive power purchased from April 2016 to November 2017 was

received from the DISCOMs. However, the data could not be segregated to ascertain specific trends

The data was extrapolated for the remaining months of FY 2017-18 based on past trend observed from

FY 2016-17. Such data for FY 2017-18 has been considered as our base of forecasting power purchased

from open access for the future years

Based on consultation with stakeholders in the state, it has been concluded that the decrease in power

purchased from open access may have bottomed out and the growth will thus remain stagnant till any

further orders on surcharges are passed by RERC

For captive power plants, with stringent environmental norms in place, it is anticipated that many captive

power plants who won’t be able to meet the norms will have to be shut down. And with improved

infrastructure, the resultant demand is expected to progressively shift to the grid.

Considering all the factors and assumptions, a nominal growth rate of 5% from FY 19 has been

considered for projection of power purchased from Open access and Captive consumption

Table 47: Electricity requirement (MUs) from OA and Captive


4568 2845 2987 3136 3293 3458 3630 3812 4003 4203 4413 4633 4865 5108 5364 5632

8.9. Assessment of additional demand

The impact of additional demand has been considered in this section post the development of the forecast to

further refine the results. The anticipated additional load has been added to the demand arrived by the hybrid

approach. The information on such loads has been captured by consultations, primary and secondary research,

review of infrastructure schemes, housing schemes and master plans etc. Depending on information on additional

load as available in the public domain and with certain approximations, the additional loads have been derived.

8.9.1. Housing: Chief Minister’s Jan Awas Yojana / Rajasthan housing board

Rajasthan Housing Board (RHB) was established on 24th February 1970 as an autonomous body to provide for

measures to be taken to deal with and satisfy the need of housing accommodation in the State. RHB primarily

focuses on affordable housing activities for society at large with special emphasis on economically weaker

sections. By December, 2016 RHB has taken up construction of 2,50,309 dwelling units, out of which 2,47,425

dwelling units have been completed, 2,44,892 dwelling units have been allotted and 2,31,311 dwelling units have

been handed over to applicants.


As per consultation with Housing Board officials, the electrical loads of houses which have been handed over are

already included in the demand data taken from the DISCOMs. Hence it was suggested that the additional

demand of houses which are under construction should only be considered. As per the data available upto

October 2017, 2884 houses are under construction and hand over will start from FY19. As per officials, the

houses are occupied on average post 2-3 years of hand over i.e. by FY 21.

Table 48: Data for State housing scheme as on Oct’17

EWS LIG MIG I MIG II HIG Total

Taken Up 81212 69531 41759 35666 22141 250309

Completed 80874 68108 41501 35241 21701 247425

Allotted 79617 67785 40806 35053 21631 244892

Handover 72771 60870 37687 33534 26449 231311

Under construction 338 1423 258 425 440 2884

The expected demand has been worked out as per the load estimation formula shared by RHB.

8.9.2. Review of Master Plans

A master plan comprises sector plans and each sector plan comprises several smaller schemes and projects.

Out of 190 municipal towns, master plans for 184 municipal towns have been prepared by Town Planning

Department, Depart of Urban Development and Housing, Government of Rajasthan and was approved by the

Government. Out of all the Master, for the major cities in Rajasthan, the end year of planning period is varied and

Master plans have been prepared for the period ending in the year 2025 to upto 2033 in other towns.

In order to estimate the additional load which may arrive due to implementation of planned initiatives, upon

consultation, it was understood that the focus of implementation will be centric around Jaipur and few of the major

towns.

The following figure provides the list of major towns in Rajasthan in terms of population. It can be observed that

out of the top 10 cities in Rajasthan, the combined population of other 9 cities is approximately twice than that of

Jaipur.

The premise for development of Master plans of city/ towns is by extrapolating the population of a city/ town upto

a target year and then assessing the amenities which will be required additionally to attain a level of standard of

living.

Table 49: Major towns in Rajasthan as per population

0 500000 1000000 1500000 2000000 2500000 3000000 3500000

Sri Ganganagar

Bharatpur

Alwar

Bhilwara

Udaipur

Ajmer

Bikaner

Kota

Jodhpur

Jaipur

Population (2011 census)


As per the projections provided in the Jaipur Master Plan 2025, there will be 15, 12,320 households by the year

2025 in the Jaipur master plan area covering city and other limits. The number of domestic households

considered for forecast in Jaipur City Circle and Jaipur District Circle combined for the year FY 2024-25 is 18,

10,350. Hence no additional demand as per the master plan for households has been considered.

In terms of additional social amenities proposed in the Jaipur Master Plan 2025 as per population projections,

3480 Ha of area is proposed to be utilized to develop the following: (insert references):

School- Primary, Secondary

Colleges

Technical education institutes

General hospitals

Polyclinics

Police station

To arrive at the estimated additional energy requirement due to Master plan, following assumptions have been

considered.

In the period from 2025-2032, the implementation of the master plan schemes will be in the 10 major

cities of Rajasthan

The impact of the new amenities has been considered from the year FY 2025-26 and subsequent

demand is assumed to grow at 10% Y0Y in line with the growth of Commercial categories

Since base forecast has been developed considering historical growth, major portion of the new

anticipated demand will considered in the forecast. Additionally, of the total requirement, 20% has been

assumed to be as additional demand over and above the base forecast

Since combined population of 9 major cities is ~ twice of the population of Jaipur, it has been assumed

that the trend will continue.

To account for development of Jaipur and 9 other major cities in the period from 2025, as per population

estimates given above, (3480 x 3) Ha has been assumed will be developed

The electricity demand has been worked out considering suitable loads for the amenities considered and

electricity requirement in terms of MUs has been derived and added to the base forecast.

8.9.3. Jaipur Metro Rail project

The work of Jaipur Metro Rail Project Phase I-A has completed and is in operation since 03 June, 2015. As per

data accessed from Jaipur Metro, the current load is 4.5 MVA. The Phase I-B of the Metro project is expected to

commence operation in FY19 and the load upto 6 MVA. The electrical infrastructure and connectivity with the

DISCOM is already in place and is accounted for.

The next phase is expected to commence post phase IB and as per planning, the total expected load of 20MVA

is expected by the year 2030.

The additional 14 MVA load has been added from the year 2030 with the assumption that the next phase of

operations will commence in the year 2030.

8.9.4. Smart City

Smart City Mission Launched in June 2015 with a target of 100 Cities to be developed as Smart Cities in India.

Four cities of Rajasthan namely - Jaipur, Udaipur, Kota & Ajmer has been shortlisted for development.

The Smart Cities will be implemented through a dedicated SPV through 2 strategic ways of development

Area Based Development(ABD)

Pan City Development

Area Based Development further is sub divided into 3 components


Retrofitting: Planning & Development in existing built up area

Redevelopment: Replacing of Existing Built environment

Greenfield: Development of a Vacant Area

Pan City Development: application of selected Smart Solutions to the existing city-wide infrastructure.

Major initiatives identified for implementation in the four cities.

Area Based Development (retrofitting & retrofitting model) - Sustainable Mobility Corridors, Heritage and

tourism, smart civic infrastructure, conservation of buildings, water body conservation, Environment

&eco-friendly recreational projects etc.

Pan City Development: development envisages application of selected Smart Solutions to the existing

city-wide infrastructure. Application of Smart Solutions will involve the use of technology, information and

data to make infrastructure and services better. E.g. Integrated Traffic Management, Security &

Surveillance system, Intelligent Street lights, City E-governance, Solid Waste Management, Citywide

smart utilities system etc.

The idea of smart city development in the four cities considered is majorly for retrofitting and re-development and

implementation of smart solutions. The extent of the implementation including additional load if any could not be

ascertained directly as there is lack of information in public domain. Considering the current pace of the projects,

no additional demand has been considered.

8.9.5. Delhi Mumbai Industrial Corridor

Delhi - Mumbai Industrial Corridor (DMIC) is India's most ambitious infrastructure programme aiming to develop

new industrial cities. The objective is to expand India's Manufacturing & Services base and develop DMIC as a

"Global Manufacturing and Trading Hub". The programme will provide a major impetus to planned urbanization

in India with manufacturing as the key driver. In addition to new Industrial Cities, the programme envisages

development of infrastructure linkages like power plants, assured water supply, high capacity transportation and

logistics facilities as well as softer interventions like skill development programme for employment of the local

populace. In the first phase eight new industrial cities are being developed. The programme has been

conceptualized in partnership and collaboration with Government of Japan.

The longest stretch of the 1483 km DMIC route (about 39%) passes through Rajasthan. As per the plan, two

industrial areas along with associated integrated facilities are to be developed

Khushkhera- Bhiwadi- Neemrana Investment Region

Jodhpur Pali Marwar Investment Region

As per the master plans of the two proposed regions, the development has been scheduled under three phases

Phase 1 – Initiation, pilot phase infrastructure and early development - Years 2016-2022;

Phase 2 - Core development - Years 2023-2032

Phase 3 – Mature development and completion - Years 2033-2042

The master plan of the two investment regions provides the estimated power requirement as given below

Table 50: Demand projected in the DMIC investment regions in Rajasthan

Yearly Demand (in MW) Phase 1

(2016-2022)

Phase 2

(2023- 2032)

Phase 3

(2033-2042)

Khushkhera- Bhiwadi- Neemrana16 955 1837 3877

Jodhpur Pali Marwar17 11571 64183 269000

16 Final development plan – Khushkhera - Bhiwadi- Neemrana Investment Region, August 2014 17 Master Plan-2042, For Jodhpur-Pali-Marwar Industrial Area, Rajasthan Sub Region of DMIC


The projects in Rajasthan have faced considerable delay in land acquisition which started in 2012. Even after

five years, the process has not been completed. Due to the delay in execution, the load as envisaged in Phase

1 of the project is expected to impact post the year FY 2026-27 with the assumption that the complete demand

as planned will be required. Nominal growth rate has been considered for projection upto the year FY 2032. Due

to the delay, the core development is expected to happen post 2032.

8.9.6. Rajasthan State Industrial Development and Industrial Corporation (RIICO)

RIICO is an apex organization engaged in fostering the growth of industrialization in the State. The mission of

RIICO is to catalyze planned and rapid industrialization of Rajasthan. RIICO develops industrial areas and

infrastructure facilities for the industrial units. During the Financial Year 2016-17, RIICO has acquired

1,513.58 acres of land and has developed 248.98 acres of land upto December, 2016.

Few of the recent developments by RIICO are:

Japanese park: RIICO has signed a MOU with JETRO, a Japanese Organization wherein Japanese

companies will set up their industrial units at Neemrana Industrial Area, Alwar, Rajasthan. Several

multinational companies such as Nissin, Mitsui, Daikin, Mitsubishi and Dainichi have already got land

allotted in this industrial area for establishing their units. RIICO has so far allotted 433 acre land to 47

Japanese companies in this area. Out of it, 43 companies have started commercial production as of

FY 17.

Korean Investment Zone: RIICO has signed a MoU with Korea Trade Investment Promotion Agency

(KOTRA). In pursuance of this MoU, a Korean Investment Zone in Ghiloth Industrial Area, District Alwar

has been set up covering an area of 2000 acres.

ShahajanpurNeemrana-Behror (SNB) knowledge city cum urban complex of around 1500 acre

Mahindra group has established an SEZ in partnership with RIICO in Jaipur with an expected

investment of ̀ 10,000 crore. In this SEZ, various zones shall be established for industrial units of different

sectors.

The officials of RICCO were consulted to understand the industrial activities in the State. Some of the

observations are

Industrial activity in the state is growing at historical natural growth rates and is not expected to pick up

suddenly

Most of the SEZ, Areas under RICCO have electrical infrastructure developed. However, actual demand

has not been there as new industries have not developed as had been expected

In the year FY 2016-17, for a target of 2542 acre, only about 10% of the area i.e. 248.98 has been

developed due to subdued industrial activity

Considering the above, no additional electricity demand has been added. The growth rates considered in

developing the base forecast reflects the views expressed by RICCO.

The anticipated additional electricity demand as anticipated from FY 21 to be added are as given below:

Table 51: Anticipated energy requirement due to additional demand from new projects at State level (MUs)

Head FY 21 FY 22 FY 23 FY 24 FY 25 FY 26 FY 27 FY 28 FY 29 FY 30 FY 31 FY 32

Housing 38 77 115 154 192 230 269 307 346 384 422 461

Master Plan - - - - 1079 1187 1305 1436 1579 1737 1911


Jaipur Metro - - - - - - - - - 29 29 29

Smart City - - - - - - - - - - - -

DMIC - - - - - 120 127 135 143 152 161 171

RIICO - - - - - - - - - - - -

Total 38 77 115 154 192 1429 1583 1748 1925 2144 2349 2571

8.10. Assessment of latent demand

Development of reasonable estimate for the latent demand in the system has been attempted. The trend of

historical consumption of electricity may or may not be a reliable indicator of the future trend of electricity

consumption. Latent demand is the desire to consume a product or service but due to various barriers, the desire

is not met and the consumption is curtailed. In developing economies there are various factors which may limit

electricity consumption. These can range from inadequate infrastructure such as network capacity constraints,

negative effect of income, high cost of supply, outdated technology etc.

Considering the case of a household with low income, consumption is based on priority of basic needs, thereby

impacting demand of other goods e.g. electricity. For an industry, the electricity demand may be suppressed due

to factors such as inadequate network infrastructure, insufficient power availability with distribution licensee to

cater to the demand, high cost of supply, reliability of supply etc.

Latent demand is an inherent demand but is not reflecting due to the following reasons:

Unmet – the demand from the unconnected consumers, which is present but is not realized currently

due to network, infrastructure issues, lack of policy focus etc.

Unserved – consumers are connected but complete demand is not met due to network restrictions, load

curtailments, unreliable supply and deficit in electricity availability etc.

Behavioral – Even with the given supply, the consumers are not realizing the full potential due to

behavioral usage issues.

In the following subsections, the assessment of demand has been carried out for each of the heads as mentioned

above:

1. Unmet demand

The demand from unconnected consumers constitutes a substantial portion of unrealized electricity requirement

in the system and remains latent unless connected. Availability of affordable and clean energy has been included

as one of the 17 Sustainable Development Goals (SDGs) of the United Nations, adopted at the UN Sustainable

Development Summit, September 2015. One of the targets under this goal is to ensure by 2030, universal access

to affordable, reliable and modern energy services. In this direction, the Government has also undertaken steps

to ensure that electricity access is provided.

In this respect, the demand from additional connections to be released has been already been considered in the

base forecast which has been developed.

2. Unserved demand

The assessment of unserved demand to consumers has been carried in the section of baseline correction of

historical data wherein feeder interruption data has been used to estimate the demand curtailment. In case that

curtailment had not happened, the unserved latent quantum would have reflected in the system. The methodology

undertaken and the assumptions considered has already been explained in the section on baseline correction.


3. Behavioral aspect

The figure below shows the average historical consumption of electricity per domestic consumer in the state.

While the overall consumption has been increasing, however the rate of growth has been dismal and not in line

with the growth prevalent in developing countries.

Figure 49: Average consumption per domestic consumers in Rajasthan (in kWh/Consumer/Year)

This may be due to unreliable power supply or high tariffs etc. to some extent, but the major aspect is pertaining

to usage behavior of the connected consumers. The trend has been similar for the country as a whole wherein

the average consumption per consumer has been reported as 1010 kWh/consumer/ year in FY 2014-15 and

1075 kWh/consumer/ year for FY 2015-1618.

A comparison with other developing countries (e.g. BRICS considered for comparison) shows that overall India’s

per capita consumption is very low and is even below the World average of 2674 kWh/consumer/year.

Hence the usage behavior is not reflecting the demand which should have been in comparison to a global average

or with respect to comparable economies. That difference is a latent aspect of demand due to behavioral usage

and can only be realized with change in behavior or by incentives.

For assessing the latent demand, an analysis of the average consumption per consumer per year as forecasted

in the base forecast using trend method has been analyzed.

Figure 50: Comparison of trends of consumption in Rajasthan up to FY 32

18 CEA Monthly report December 2017

-2.0%

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

0

200

400

600

800

1000

1200

1400

FY07 FY08 FY09 FY10 FY11 FY12 FY13 FY14 FY15 FY16

% g

row

th r

ate

kW

h/C

onsu

me

r/ Y

ea

r

Cosumption per Domestic consumer Rate of Growth

800

900

1000

1100

1200

1300

1400

1500

1600

FY07

FY08

FY09

FY10

FY11

FY12

FY13

FY14

FY15

FY16

FY17

FY18

FY19

FY20

FY21

FY22

FY23

FY24

FY25

FY26

FY27

FY28

FY29

FY30

FY31

FY32

kW

h/c

onsum

er/

year

Current trend based on historical usage

Trend in case per capita consumption increases usage improves


Historically, over the 10 year period, average CAGR in consumption per consumer for the state has been about

3.6%. In the base forecast, the same has already been considered and projected.

However, for the latent consumer demand to start realizing, that the current rate of growth has to increase as the

economy progresses. But changes related to behavior doesn’t happen all of a suddenly in a period of 1-3 years

and such effects are noticeably observed over long term horizons.

Hence for the present scenario, to arrive at an assessment of the latent demand, the following assumptions have

been considered

The current CAGR growth rate of 3.6% has been assumed to increase by 10% YoY from FY 18. Since

there is no definite literature available pertaining to latent demand, the assumption has been considered

based on a realistic scenario

The assumption is also reflective of the fact that in case the CAGR growth improves, it will be gradual

over short term

Based on the above assumption, the YoY growth rate has been arrived at for all the years.

The difference in consumption per consumer has been worked out for the base case (historical) and case

wherein increased CAGR growth rate is considered.

The difference upon multiplication with the number of domestic consumers gives the additional latent

demand which would be incident on the system.

Table 52: Estimated latent demand due to behavioral aspect for Rajasthan (in MUs)


- 273 389 533 728 850 1089 1368 1690 2052 2354 2800 2324 2764 3285 3906

8.11. Impact of Energy Efficiency

The assessment of the impact of energy efficiency has been carried out using efficiency factors for different

categories while developing the forecast. To decide on the factors, various literature were reviewed to understand

the actual impact of efficiency across various consumer categories. However, most of the study reports highlight

potential of energy efficiency across major categories which are worked out based on estimates and assumptions.

There was also a wise variation in the savings potential reported due to the inherent assumptions and

methodologies adopted. The potential is also dependent on the quantum of investment and replacements which

are required to achieve the same.

The energy savings potential across different consumers categories are as given:

Table 53: Electricity Savings potential across categories19

Sl. No. Consumer category Electricity savings potential

1. Domestic urban 15-20%

2. Domestic rural 40-50%

3. Commercial buildings 20%

4. Public lightings 50%

5. Agriculture 30%

5. PWW 20-25%

6. Industry 7-10%

19 BEE, NPC study 2009 and EESL study


With these benchmarks in place, a realistic approach was considered and the share of demand to be impacted

by energy efficiency was worked out. A YoY increase in % share has been considered with assumption that the

impact will increase as we progress over the years. The awareness towards efficiency will increase and there

will be more penetration and acceptance of energy efficiency products. The following table provides the YoY %

of energy efficiency considered across categories.

Table 54: % savings estimated due to energy efficiency

Category FY17 FY18 FY19 FY20 FY21 FY22 FY23 FY24

Domestic 2.0% 2.3% 2.6% 3.0% 3.5% 4.0% 4.6% 5.3%

Non-Domestic 1.0% 1.1% 1.1% 1.2% 1.2% 1.3% 1.3% 1.4%

PSL 1.0% 1.2% 1.4% 1.7% 2.1% 2.5% 3.0% 3.6%

Agri 1.0% 1.1% 1.1% 1.2% 1.2% 1.3% 1.3% 1.4%

Industry 0.5% 0.5% 0.5% 0.5% 0.6% 0.6% 0.6% 0.6%

PWW 1.0% 1.1% 1.2% 1.3% 1.5% 1.6% 1.8% 1.9%

Mixed load 1.0% 1.1% 1.1% 1.2% 1.2% 1.3% 1.3% 1.4%


Domestic 6.1% 7.0% 8.1% 9.3% 10.7% 12.3% 14.2% 16.3%

Non-Domestic 1.5% 1.6% 1.6% 1.7% 1.8% 1.9% 2.0% 2.1%

PSL 4.3% 5.2% 6.2% 7.4% 8.9% 10.7% 12.8% 15.4%

Agri 1.5% 1.6% 1.6% 1.7% 1.8% 1.9% 2.0% 2.1%

Industry 0.6% 0.7% 0.7% 0.7% 0.7% 0.7% 0.8% 0.8%

PWW 2.1% 2.4% 2.6% 2.9% 3.1% 3.5% 3.8% 4.2%

Mixed load 1.5% 1.6% 1.6% 1.7% 1.8% 1.9% 2.0% 2.1%

8.12. Development of forecast scenarios

The forecasts have been developed with two scenarios:

Scenario 1: Forecast using baseline corrected data – unrestricted forecast (Served +

Unserved)

Since the baseline of the historical (served or restricted) data had been corrected by adding the

unserved demand (derived from feeder data), the forecasts arrived at for this Scenario is unrestricted

forecast i.e. the forecast of the future years contains demand from both served and unserved units.

The scenario is reflective of the case when all the unserved demand currently not served by the utilities

is fulfilled by way of no interruption of supply, no curtailments etc. This scenario is also useful in case

uninterrupted supply is provided to consumers e.g. 24 hours for domestic, 8 hours for agriculture etc.


Scenario 2: Forecast after adjusting for anticipated unserved demand in the future

Scenario 2 = Scenario 1 – Future unserved demand

In this scenario, it has been assumed that although with improvement in the supply situation and

infrastructure, the share of unserved demand will decrease progressively. However, the reduction will

be more gradual and hence across the states/ circles, there will be some amount of unserved demand

that will remain, even though overall, there will be a net decrease. The unserved demand for the

forecast period has been derived by considering an improving trend of supply hours. It has been

assumed that the supply hours will improve by 1% w.r.t previous year. The unserved demand derived

for the forecast period has been subtracted from the unrestricted forecast results to arrive at future

restricted results.

The Scenario 2 has been derived by continuing with the same assumption that the gap in supply hours

will decrease by 1% YoY from FY 2016-17. It was observed that while in some urban circles like JPDC

etc. the unserved demand will be nil for domestic categories, in other circles certain % will remain.

The resultant net unserved demand for each Circle have been calculated and subtracted from the

unrestricted forecast results.

The figure below highlights an illustration of the same

A = Served or Restricted demand

B = Unserved demand

Scenario 1 = A + B

Scenario 2 = A = Scenario 1 – B

Figure 51: Illustration of served - unserved demand for the state

0

20000

40000

60000

80000

100000

120000

Ele

ctr

icity c

on

su

mo

tio

n (

MU

s)

Served Unserved

B

A


8.13. Estimation of the future loss trajectory

One of the gap identified in estimation of loss trajectory is that the trajectories are developed based on top down

targets. Historically, it has been observed that the targets are not met as per the defined timelines and are also

periodically revised. Due to which the overall forecasted results vary with that of the actual. Hence in the present

section, the future loss trajectories have been estimated based on realistic estimates after analysis of the actual

loss data.

The data accessed from the utilities have yearly wise % of distribution loss, transmission loss and transmission

and distribution loss figures. The data doesn’t have any segregation of losses for specific activities like theft,

faulty reading etc.

In the current assignment, the trajectory has been developed based on analysis of available data, trends

observed and realistic assumptions after consultation.

For the state, the historical losses are given in the following table

Figure 52: % Actual T&D loss for the state20

The estimation of loss trajectory has been carried out after considering the following points:

The state has a target to achieve distribution loss target of 15% upto FY19 whereas at the end of FY17,

the actual distribution loss achieved is 23.3%. As per estimates, the state is expected to reach the

distribution loss level of 19.3% by end of FY18.

It has been highlighted that the reduction of loss from 25% to 20% is possible at a faster rate because

same can be achieved by monitoring thefts, leakages and by ensuring metering. However, the reduction

below 20% to 15% will be more gradual and require investment in improving the network infrastructure.

Reduction below 15% distribution loss at a state level is not envisaged as of now.

The transmission loss has ranged between 5-6% in the past 10 years and any further reduction will be

very gradual and may reach a limit of 3% for the state.

Keeping in mind the above, the projected trajectory for the state is given as below:

Table 55: Projected loss trajectory for the state


Distribution % 23.3% 20.0% 19.0% 18.0% 17.0% 16.0% 15.0% 15.0%

Transmission % 4.0% 5.2% 5.0% 4.8% 4.6% 4.4% 4.2% 4.1%

20 Data accessed from distribution utilities

34.7%31.4%

26.6% 26.1%

22.3%19.6% 19.2%

24.1%27.2% 27.7%

23.3%

5.4% 6.1% 6.6% 6.2% 6.1% 5.7% 5.6% 4.5% 5.8% 5.2% 4.0%

38.3%35.5%

31.5% 31.0%

26.8%24.3% 23.8%

27.5%

31.4% 31.4%

26.3%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

FY07 FY08 FY09 FY10 FY11 FY12 FY13 FY14 FY15 FY16 FY17

% L

oss

% Distribution loss % Transmission loss % T&D loss


T&D % 26.3% 23.5% 24.0% 22.8% 21.6% 20.4% 19.2% 19.1%

Category FY25 FY26 FY27 FY28 FY29 FY30 FY31 FY 32

Distribution % 15.0% 15.0% 15.0% 15.0% 15.0% 15.0% 15.0% 15.0%

Transmission % 3.9% 3.8% 3.6% 3.5% 3.3% 3.2% 3.1% 3.0%

T&D % 18.9% 18.8% 18.6% 18.5% 18.3% 18.2% 18.1% 18.0%

Based on the above T&D losses worked out for each year, energy requirement for the state has been calculated

using the following given below.

Energy requirement = Forecasted Energy consumption / (1- T&D Loss %)

In the table below, energy requirement for the state has been calculated considering the results derived using

Trend – restricted method. Similarly, results from other methods can also be calculated.

Table 56: Calculation of energy requirement for the state


Electricity

consumption 55,383 57,634 61,931 66,609 71,720 75,845 80,265 84,891

T&D Loss % 26.3% 23.5% 24.0% 22.8% 21.6% 20.4% 19.2% 19.1%

Electricity

requirement 75,169 75,376 81,479 86,272 91,480 95,302 99,387 104,895


Electricity

consumption 89,706 96,011 101,052 106,476 111,024 116,655 122,604 129,568

T&D Loss % 18.9% 18.8% 18.6% 18.5% 18.3% 18.2% 18.1% 18.0%

Electricity

requirement 110,622 118,169 124,144 130,577 135,924 142,586 149,624 158,009

8.14. Estimation of load factor and peak demand

Load Factor is the ratio between average energy consumption rate (average load) and peak energy consumption

rate (peak load) over a specified period of time and can apply to daily, monthly, seasonal or annual periods.

In the present case, the bottom up approach entailed undertaking forecasts at circle levels and then aggregating

upto the utility and state level. To estimate the peak demand that may be incident on the system for the forecast

years, commonly used forecasting methodologies assume average load factor and then peak demand is derived

which is a co-incident peak demand.

In this section two approaches have been used:


Traditional approach: in this approach, the load factor on an average yearly basis is worked out based

on historical average load factor data and then peak demand is derived using the energy requirement

(forecasted) and load factor using the following

Peak Demand (MW) = Electricity required (MUs) / (Load Factor x Time in hours)

Depending on the period (daily, monthly, annual etc.) for which the calculation is made, the peak demand

can be derived.

Forecasting approach: to determine peak demand by forecasting methods using historical peak

demand data

1. Traditional approach

In this approach, the year wise load factor data was accessed and analyzed to understand the trend. For the

state of Rajasthan, the following table provides the yearly data

Table 57: Rajasthan: Load factor data21

State FY09 FY10 FY11 FY12 FY13 FY14 FY15 FY16

Monthly

LF, avg. of

2008-15

Daily LF,

avg. of

2008-15

Rajasthan 0.69 0.68 0.70 0.73 0.71 0.69 0.71 0.70 0.80 0.89

As per the results of the study conducted by POSOCO, the load factor in the state in increase. The same is

already evidenced from the decomposition of the monthly data provided in the same report.

The figure below shows the increase in trend of load factor for the state.

Figure 53: Rajasthan: Decomposition of load factor trend (POSOCO report)22

21 Electricity load factor in Indian Power System, POSOCO, January 2016 22 Electricity load factor in Indian Power System, POSOCO, January 2016, available in public domain


In view of the trend observed, a nominal increasing trend has been considered, derived from past historical load

factor data and projected to arrive at the average load factor for the state

Figure 54: Estimated load factor for the state


0.702 0.704 0.706 0.708 0.711 0.713 0.715 0.717 0.719 0.721 0.723 0.726 0.728 0.73 0.732 0.734

Using the load factors derived as above, the co-incident peak demand of the state was determined using the

formula provided. The table below provides the annual coincident peak demand calculated using the trend –

restricted approach using the electricity forecast and load factor as determined above.

Table 58: Annual Peak demand for state: Using forecasting results using Trend method

FY17 FY18 FY19 FY20 FY21 FY22 FY23 FY24 FY25 FY26 Fy27 FY28 FY29 FY30 FY31 FY32

12224 12222 13175 13910 14688 15258 15868 16701 17563 18710 19601 20532 21314 22297 23334 24574

Similarly, peak demand derived for all the scenarios is given in the section on summary electricity forecast results.

The next step is to use a diversity factor to further normalize the demand as it will be unlikely that all the demand

will be coincident on the system at the same time. As per the report by POSOCO, diversity factor recognizes that

the whole load does not equal the sum of its parts due to time interdependence. The concept of being able to de-

rate a potential maximum load to an actual maximum demand is known as the diversity factor.

Diversity factor can be represented as follows

Diversity factor = (sum of individual maximum demand)/ (simultaneous max. demand)

The POSOCO report provides yearly historical data for diversity factor for the Western region of the country as a

whole. The diversity factor for the western region is in the range of 1.02 to 1.06 for the period FY 2009-14. The

report doesn’t provide any trend of diversity factor for the region/ state nor monthly data for could be accessed to

analyze and consider a trend. Due to the lack of data and trend available, diversity factor has not be considered

to arrive at the diversity adjusted peak demand for the state.

2. Direct forecasting approach

In this approach, monthly historical peak demand data for the state from the year FY 2009-10 to FY 2016-17 has

been considered and using statistical forecasting methods, the forecast of expected peak demand for the state

has been derived. The present method is not reliant on energy requirement or load factor values and is based

on past seasonality and trends. The table below provides the historical peak demand of Rajasthan

Table 59: Unrestricted peak demand of Rajasthan23

April May June July Aug. Sept. Oct. Nov. Dec. Jan. Feb. Mar.

FY10 5364 5971 5968 6487 6240 5453 6087 6143 6771 6798 6859 6567

FY11 6159 6821 6215 6171 5211 5426 6275 6361 7116 7582 7729 7549

FY12 6452 6800 7054 6736 6443 7536 7627 8188 7556 8940 7779 7827

FY13 7143 7511 7686 7765 7044 7464 7454 7671 8511 8940 8396 8433

FY14 7512 7799 7626 7306 7206 8929 7899 8627 9735 9977 10047 9000

FY15 7977 8844 9131 9403 10087 10188 9339 9525 10642 10179 10095 8199

23 Source: NRPC, available in public domain


FY16 7798 8577 9010 8431 10080 10961 9453 9780 10123 10720 10190 10200

FY17 9027 9690 9906 9288 7807 9816 10000 10300 10700 11300 11100 10600

Figure 55: Historical peak demand for the state of Rajasthan

The data highlights that there is a trend and a seasonal component in the peak demand and it is important that

such trends are captured using forecasting methods. Also, there is a wide variation in the peak demand across

months. In FY 17, the peak demand was maximum during January and minimum in August. To ensure that such

trends and seasonality are reflected in the forecast, the Holt-Winter’s method was used to forecast the monthly

peak demand in the state for the forecast period. The monthly forecast results are provided in the table below:

Table 60: Monthly peak demand for the state determined using forecasting methods

MW April May June July Aug. Sept. Oct. Nov. Dec. Jan. Feb. Mar.

FY18 10001 10386 10601 9327 8716 11275 11569 11032 11443 12267 11934 12533

FY19 11966 12488 12794 12000 11785 14137 14080 13782 14244 14927 14576 14593

FY20 13317 13826 14118 13314 13088 15431 15365 15059 15513 16189 15832 15841

FY21 14506 15013 15303 14496 14267 16608 16540 16232 16684 17358 16999 17008

FY22 15643 16149 16439 15631 15403 17743 17675 17366 17818 18492 18133 18142

FY23 16759 17266 17556 16749 16521 18861 18794 18486 18939 19613 19255 19264

FY24 17869 18377 18668 17862 17634 19976 19909 19603 20056 20731 20374 20384

FY25 18981 19489 19781 18977 18750 21093 21028 20722 21177 21853 21497 21508

FY26 20098 20608 20901 20098 19873 22217 22153 21848 22304 22982 22627 22639

FY27 21224 21735 22030 21228 21004 23349 23286 22983 23440 24119 23766 23779

FY28 22361 22873 23169 22368 22146 24492 24431 24129 24587 25268 24915 24930

FY29 23508 24022 24319 23520 23299 25647 25587 25286 25746 26428 26077 26093

FY30 24668 25183 25482 24684 24464 26814 26755 26456 26917 27600 27250 27268

FY31 25841 26357 26657 25861 25642 27993 27936 27638 28100 28785 28436 28455

FY32 27026 27544 27846 27050 26833 29185 29129 28833 29297 29982 29635 29655

4000

5000

6000

7000

8000

9000

10000

11000

12000A

pr-

09

Jul-

09

Oct-

09

Jan

-10

Ap

r-10

Jul-

10

Oct-

10

Jan

-11

Ap

r-11

Jul-

11

Oct-

11

Jan

-12

Ap

r-12

Jul-

12

Oct-

12

Jan

-13

Ap

r-13

Jul-

13

Oct-

13

Jan

-14

Ap

r-14

Jul-

14

Oct-

14

Jan

-15

Ap

r-15

Jul-

15

Oct-

15

Jan

-16

Pe

ak d

em

an

d in

MW

Peak demand


8.15. Finalization of forecast results

The forecast developed using multiple methods have been finalized as per the following steps:

Unrestricted forecast were developed for the three utilities using the three methods (Scenario 1). Such

forecasts have been developed at a Circle level and aggregated to arrive at the utility level forecast

From the unrestricted forecast, quantum of unserved demand (as explained in Scenario 2) have been

subtracted for each circles across the consumer categories to arrive at the restricted forecast. The

restricted forecast for each Circles were then summed up to arrive at the forecast at the distribution utility

level and subsequently at the state level.

The results obtained for Scenario 1 and Scenario 2 were based on bottom up approach. Since additional

demand like electricity consumption from infrastructure, open access and captive, electric vehicles, latent

demand, demand from railways etc. have been calculated separately, such demands were then added

to the aggregated utility demand to arrive at the total electricity consumption forecast for the state.

Forecasted electricity consumption for the state = Demand from three Utilities + Additional demand

The above approach was used to arrive at the state demand for all the options and scenarios.

The derived results were then compared with that of actual data. The following table provides a

comparison of the forecast values at restricted (R) level with that of actual data (from Utilities) for the

base year FY 17.

50000

70000

90000

110000

130000

150000

FY 16(BaseYr)

FY 18 FY 20 FY 22 FY 24 FY 26 FY 28 FY 30 FY 32

Trend- UR Trend- R HW - UR HW - R

Econometric - UR Econometric - R CEA-19th EPS

UR – Unrestricted R - Restricted


Table 61: FY 17: Actual vs Forecast for 3 methods

FY 17 JVVNL AVVNL JdVVNL State*

Method Actual

(MUs)

For.

(MUs)

Varia-

tion

Actual

(MUs)

For.

(MUs)

Varia-

tion

Actual

(MUs)

For.

(MUs)

Varia-

tion

Actual

(MUs)

For.

(MUs)

Varia-

tion

Trend -R 18496 18897 2.2% 13786 13785 0.0% 17732 17700 -0.2% 50015 50382 0.7%

HW-R 18496 19045 3.0% 13786 14110 2.4% 17732 17260 -2.7% 50015 50415 0.8%

Econometric

- R 18496 18844 1.9% 13786 14054 1.9% 17732 17570 -0.9% 50015 50468 0.9%

* State demand cumulative of 3 DISCOMs, excluding additional demand

The variation for the restricted demand condition (Scenario 2) is in the range of 0-3%. Since the forecast

for Scenario 1 is higher than that of Scenario 1, the variation will be more in case of Scenario 1.

The restricted energy consumption forecasted at state level was then grossed up by T&D losses to arrive

at the energy required at the state boundary.


8.16. Validation of results with traditional methodologies

For validation of the forecast results, the forecast developed by CEA in the 19th EPS has been used. CEA in the

19th EPS has provided year wise forecast for the period FY 2015-16 to FY 2026-27 and also perspective electricity

demand projections for the years FY 2031-32 and FY 2036-37.

In the following table, the comparison of forecast results with results of CEA is shown for comparison.

Table 62: Comparison of forecast results with 19th EPS results for the State (MUs)

Year FY16

(Base Year)

FY 17 FY 18 FY 19 FY 20 FY 21 FY 22 FY 23 FY 24 FY 25 FY 26 FY 27

UR-Tr 57855 65737 68146 72609 77453 82745 86783 91088 95563 100188 106261 111004

3.7% 6.5% 6.7% 6.8% 4.9% 5.0% 4.9% 4.8% 6.1% 4.5%

R-Tr 55383 57634 61931 66609 71720 75845 80265 84891 89706 96011 101052

4.1% 7.5% 7.6% 7.7% 5.8% 5.8% 5.8% 5.7% 7.0% 5.3%

UR-Eco

57855 65015 67673 72353 77301 82636 87935 93513 99186 105129 112577 118613

4.1% 6.9% 6.8% 6.9% 6.4% 6.3% 6.1% 6.0% 7.1% 5.4%

R-Eco

54906 57361 61849 66615 71763 76947 82472 88169 94188 101782 108036

4.5% 7.8% 7.7% 7.7% 7.2% 7.2% 6.9% 6.8% 8.1% 6.1%

HW 54942 57186 61197 65328 69667 74203 78936 83629 88473 94694 99521

4.1% 7.0% 6.8% 6.6% 6.5% 6.4% 5.9% 5.8% 7.0% 5.1%

CEA 48017 51971 55872 59573 63962 68648 73709 79184 85025 91399 98449 106051

7.5% 6.6% 7.4% 7.3% 7.4% 7.4% 7.4% 7.5% 7.7% 7.7%

The results developed using the forecast methodologies are inclusive of additional demand i.e. Open

Access & Captive, Railways, Electric vehicles, Delhi Mumbai Industry Corridors, Master Plan of cities,

Latent demand, Housing schemes, Metro rail etc. While CEA in their study has also considered some of

the additional demand, however state specific additions could not be determined separately from the

results of 19th EPS.

The higher demand in FY17 in comparison to CEA results is also due to higher actual demand from Open

access consumers in the state and due to addition of latent demand, which is not included in 19th EPS.

From the results it can be concluded that the forecast using traditional approach is comparable to the

electricity consumption requirement developed under the restricted condition (Scenario 2).

The forecast results arrived at for unrestricted condition (Scenario 1) is higher is comparison to the results

presented in the 19th EPS. It can be concluded from the results that forecast using baseline corrected

historical data (unrestricted condition) will lead to higher forecast results and may not represent a realistic

case.

Scenario 2 (as explained in earlier sections) is arrived at after subtracting the net unserved demand from

the unrestricted forecast. The scenario is more reflective of the current situation.

While the growth rates (YoY) in 19th EPS is more uniform. However, in alternate methodologies, this is

not the case as the approach and methodology is different and also some of the methods used do not

use YoY growth rates while arriving at the forecast results.


Since the methodologies adopted are different, there will be difference in the results which are arrived

at. Hence a direct validation of results using YoY growth rates or derived forecast values is not

recommended.

Following table provides a comparison for the year FY 32. The range of results obtained for the terminal

year FY32 is also very broad and any of the results can be a possibility going forward.

Table 63: Comparison of forecasts for year FY32 (MUs)

Trend - UR Trend - R HW-R Econometric-R Econometric-UR 19th EPS

FY 32 137916 129568 124553 143496 152578 138933

The forecast of electricity demand for the state and the derived peak demand was compared with the results of

19th EPS. A comparative table is given below:

Electricity demand Peak demand

FY22 FY27 FY32 FY22 FY27 FY32

UR - Tr 17.7% 4.7% -0.7% 20.9% 7.0% -1.6%

R - Tr 2.9% -4.7% -6.7% 5.7% -2.6% -7.5%

UR - Eco 19.3% 11.8% 9.8% 22.6% 14.3% 8.9%

R - Eco 4.4% 1.9% 3.3% 7.2% 4.1% 2.4%

HW 0.7% -6.2% -10.4% 3.4% -4.1% -11.1%


9. Summary of demand forecast results

The following results have been covered in this section:

Forecasted electricity consumption for Scenario 1 (unrestricted) and Scenario 2 (restricted) condition for

Rajasthan

Derived YoY rates have been highlighted

The results of 19th EPS has also been highlighted for the State and Utility level for comparison

Forecasted electricity consumption Scenario 1 (unrestricted) and Scenario 2 (restricted) condition for

three utilities – JVVNL, AVVNL, JdVVNL

Forecasted electricity consumption (restricted condition) for each of the 26 circles

The results developed from each of the three methods for each case as mentioned above


9.1. Rajasthan – Forecast summary

9.1.1. Forecast of Electricity consumption

(MUs) Scenario FY 17 FY 18 FY 19 FY 20 FY 21 FY 22 FY 23 FY 24 FY 25 FY 26 FY 27 FY 28 FY 29 FY 30 FY 31 FY 32

Domestic UR-Tr 12574 13488 14430 15389 16356 17263 18136 18953 19695 20340 21001 21543 21970 22315 22655 23159

R-Tr 11278 12227 13219 14245 15275 16265 17239 18174 19050 19844 20655 21356 21860 22254 22655 23159

UR-Eco 12473 13371 14298 15240 16196 17176 18167 19168 20177 21190 22176 23157 24118 25046 25900 26713

R-Eco 11163 12097 13075 14085 15106 16164 17252 18369 19512 20680 21835 23016 24056 25010 25890 26713

UR-HW 12222 13187 14037 14862 15665 16460 17231 17975 18688 19368 19990 20572 21086 21527 21864 22115

CEA 11534 12548 13526 14563 15568 16603 17774 18982 20228 21626 23068 - - - - -

Non-Domestic

UR-Tr 5368 5785 6233 6715 7233 7692 8179 8693 9235 9807 10436 11101 11804 12546 13329 14161

R-Tr 4270 4658 5080 5539 6037 6496 6987 7511 8070 8666 9323 10025 10776 11575 12427 13341

UR-Eco 5272 5771 6300 6850 7432 7979 8558 9156 9781 10434 11077 11737 12425 13142 13889 14816

R-Eco 4181 4633 5119 5632 6184 6717 7290 7888 8523 9195 9872 10576 11319 12102 12928 13937

UR-HW 5128 5469 5819 6159 6503 6839 7181 7515 7847 8178 8517 8846 9175 9502 9829 10154

CEA 4398 4883 5420 6075 6808 7628 8594 9685 10921 12370 14008 - - - - -

PSL Tr 420 437 453 470 486 501 517 533 548 562 566 564 556 539 513 475

Eco 447 460 498 538 579 620 663 707 753 800 847 896 944 991 1037 1082

HW 458 500 541 581 620 659 696 733 768 800 832 860 884 905 920 929

CEA 345 265 290 318 349 384 421 463 509 560 615 - - - - -

Agri (M+N+P)

UR-Tr 25496 27628 29937 32439 35150 37055 39048 41131 43304 45570 47735 49981 52306 54709 57189 60433

R-Tr 18512 20334 22331 24520 26918 28751 30691 32743 34909 37195 39444 41804 44277 46863 49565 52985

UR-Eco 25241 27422 29683 32003 34428 36819 39262 41732 44248 46820 49475 52127 54806 57503 60206 63037

R-Eco 18274 20120 22075 24113 26285 28472 30755 33101 35534 38064 40722 43425 46204 49055 51968 55050


UR-HW 25754 28003 30120 32204 34319 36397 38509 40581 42648 44711 46876 48932 50984 53031 55075 57114

CEA 21717 23308 24514 26154 27958 29946 31977 34109 36481 39038 41786 - - - - -

Agri (F) UR-Tr 3508 3099 2746 2440 2174 1935 1733 1560 1411 1282 1158 1054 965 887 818 650

R-Tr 2532 2268 2037 1835 1657 1495 1357 1237 1134 1043 955 880 815 758 707 569

UR-Eco 2674 2234 1912 1718 1653 1620 1616 1540 1546 1557 1569 1590 1613 1640 1668 1699

R-Eco 1932 1637 1421 1294 1262 1252 1266 1221 1241 1266 1292 1325 1360 1399 1440 1483

R-HW 2916 2266 1907 1713 1649 1615 1612 1535 1541 1552 1565 1585 1609 1635 1664 1694

CEA In EPS, Agri (F) is not given separately

Industry (S + M)

Tr 3098 3231 3371 3518 3673 3818 3969 4126 4289 4460 4631 4809 4994 5187 5387 5617

Eco 3252 3395 3544 3700 3859 4028 4205 4388 4578 4768 4969 5175 5387 5603 5818 6043

HW 3276 3422 3575 3734 3896 4069 4247 4430 4618 4805 5001 5201 5404 5610 5813 6025

CEA 3160 3362 3575 3803 4045 4303 4577 4869 5179 5509 5860 - - - - -

Industry (L)

Tr 7721 8197 8707 9256 9847 10214 10594 10989 11398 11822 12317 12833 13372 13935 14522 15155

Eco 8029 8584 9171 9747 10319 10912 11524 12158 12818 13493 14033 14784 15577 16415 17288 18234

HW 7935 8457 8999 9516 10013 10515 11015 11515 12017 12507 12832 13333 13837 14345 14840 15354

CEA 7771 8300 8867 9474 10123 10819 11564 12362 13216 14132 15113 - - - - -

PWW Tr 1901 2004 2111 2224 2341 2400 2458 2515 2571 2624 2668 2709 2746 2778 2806 2810

Eco 1942 2075 2212 2353 2499 2655 2817 2992 3175 3368 3577 3800 4044 4300 4582 4885

HW 1924 2034 2143 2252 2357 2463 2567 2672 2775 2873 2974 3071 3170 3263 3357 3448

CEA 1916 2037 2173 2328 2506 2694 2902 3134 3396 3690 4018 - - - - -

Mixed Load

Tr 649 665 682 699 716 726 737 748 759 770 781 791 802 813 824 835

Eco 683 746 796 850 901 947 983 1029 1076 1124 1175 1231 1281 1338 1394 1446

HW 683 746 796 850 901 947 983 1029 1076 1124 1175 1231 1281 1338 1394 1446


CEA 676 698 717 737 759 780 802 825 848 872 896 - - - - -

Total for Utilities

UR-Tr 60736 64533 68671 73151 77975 81605 85371 89247 93210 97236 101292 105385 109515 113710 118042 123294

6.3% 6.4% 6.5% 6.6% 4.7% 4.6% 4.5% 4.4% 4.3% 4.2% 4.0% 3.9% 3.8% 3.8% 4.4%

R-Tr 50382 54021 57993 62306 66950 70666 74548 78575 82727 86986 91340 95772 100198 104703 109406 114946

7.2% 7.4% 7.4% 7.5% 5.6% 5.5% 5.4% 5.3% 5.1% 5.0% 4.9% 4.6% 4.5% 4.5% 5.1%

UR-Eco 60576 64736 69189 73929 78970 84072 89326 94696 100331 106172 111974 118162 124548 131153 137969 145308

6.9% 6.9% 6.9% 6.8% 6.5% 6.2% 6.0% 6.0% 5.8% 5.5% 5.5% 5.4% 5.3% 5.2% 5.3%

R-Eco 50467 54423 58685 63243 68097 73084 78285 83679 89390 95376 101396 107891 114526 121388 128531 136226

7.8% 7.8% 7.8% 7.7% 7.3% 7.1% 6.9% 6.8% 6.7% 6.3% 6.4% 6.1% 6.0% 5.9% 6.0%

HW 49941 53573 57259 61026 64897 69025 73219 77313 81494 85669 89810 94018 98113 102149 106120 109931

7.3% 6.9% 6.6% 6.3% 6.4% 6.1% 5.6% 5.4% 5.1% 4.8% 4.7% 4.4% 4.1% 3.9% 3.6%

CEA 51971 55872 59573 63962 68648 73709 79184 85025 91399 98449 106051 - - - - 138933

7.5% 6.6% 7.4% 7.3% 7.4% 7.4% 7.4% 7.5% 7.7% 7.7%

Addl. Consumption to be added to above

5001 3613 3938 4303 4770 5178 5717 6316 6979 9025 9712 10704 10826 11952 13198 14622

Total for State

UR-Tr 65737 68146 72609 77453 82745 86783 91088 95563 100188

106261

111004 116090 120342 125662

131240

137916

3.7% 6.5% 6.7% 6.8% 4.9% 5.0% 4.9% 4.8% 6.1% 4.5% 4.6% 3.7% 4.4% 4.4% 5.1%

R-Tr 55383 57634 61931 66609 71720 75845 80265 84891 89706 96011 101052 106476

111024

116655 122604 129568

4.1% 7.5% 7.6% 7.7% 5.8% 5.8% 5.8% 5.7% 7.0% 5.3% 5.4% 4.3% 5.1% 5.1% 5.7%

UR-Eco 65015 67673 72353 77301 82636 87935 93513 99186 105129 112577 118613 125202 131020 137930 144980 152578

4.1% 6.9% 6.8% 6.9% 6.4% 6.3% 6.1% 6.0% 7.1% 5.4% 5.6% 4.6% 5.3% 5.1% 5.2%

R-Eco 54906 57361 61849 66615 71763 76947 82472 88169 94188 101782 108036 114931 120997 128165 135542 143496


4.5% 7.8% 7.7% 7.7% 7.2% 7.2% 6.9% 6.8% 8.1% 6.1% 6.4% 5.3% 5.9% 5.8% 5.9%

HW 54942 57186 61197 65328 69667 74203 78936 83629 88473 94694 99521 104722 108939 114101 119318 124553

4.1% 7.0% 6.8% 6.6% 6.5% 6.4% 5.9% 5.8% 7.0% 5.1% 5.2% 4.0% 4.7% 4.6% 4.4%

CEA 51971 55872 59573 63962 68648 73709 79184 85025 91399 98449 106051 - - - - 138933

7.5% 6.6% 7.4% 7.3% 7.4% 7.4% 7.4% 7.5% 7.7% 7.7%

Note:

UR – Unrestricted electricity consumption in MUs

R – Restricted electricity consumption in MUs

Tr – Trend method

Eco – Econometric method

HW – HoltWinters Method

Additional consumption – Open Access & Captive, Railways, EV, DMIC, Master Plans, Latent demand, Metro etc.

The % shown are YoY growth rates derived from the forecast results.

Interpretation of forecast results

The results developed using the forecast methodologies are inclusive of additional demand i.e. Open Access & Captive, Railways, Electric vehicles, expected

infrastructure plans, Master Plan of cities, Latent demand, Housing schemes, Metro etc. While CEA in their study has also considered some of the additional

demand, however state specific additions could not be determined separately from the results of 19th EPS.

Higher demand in FY17 compared to CEA is due to baseline correction, actual demand from Open access, latent demand etc. that is not included in 19th EPS

In the case of an unrestricted scenario forecast, the forecasted demand provides an estimate of the total consumer demand which may be incident on the system

in case there is no unserved or unmet demand. In such a scenario, all consumers will be using electricity as per the potential and there is no demand, which is

unserved due to any factors.

In case of a restricted forecast, the forecasted demand provides an estimate of the consumer demand which will be incident on the system in case some consumers

are still not electrified or required number of supply hours is not met for electrified consumers.


Considering an example for econometric method, in the year FY17, the unrestricted forecast is 65015 MUs and restricted forecast is 54906 MUs. It implies that a

demand of 10109 MUs (65015 – 54906) or 15.55% of unrestricted demand is not being served or met by the distribution utilities even though the demand existed.

In FY 32, for the same method, a demand of 9082 MUs (152578 – 143496) or 6% of unrestricted demand will not be met. This also implies that an improvement

in supply situation is envisaged thereby leading to improved meeting of consumer demand.

Baseline correction has enabled forecasting of the unrestricted forecast, as otherwise only a restricted forecast would have been possible with the restricted

historical data. The restricted forecast would only show that demand would increase from 54906 MUs in FY17 to 143496 MUs. With a restricted result, the total

consumer demand cannot be inferred.

Currently the planning for additional capacities is based on the demand forecasting results of EPS. Since the demand forecasting methodology adopted in EPS

does not consider the baseline correction of historical data, the total consumer demand for future may not be captured fully since unmet, latent demand etc. are

not considered currently. There may be variations between what is projected and the actuals which might occur. In a future scenario, electricity demand forecasting

without baseline correction may lead to shortfall in reserve capacities. The shortfall will impact the reliability of supply to consumers in future. Therefore, adoption

of alternate methodologies that can minimize such variations will lead to a better estimate and can then be used to plan for adequate reserves in the system.

Planning of adequate reserves proactively would ensure that reliable and quality power may be supplied in future in line with Government’s 24 x 7 Power for All

(PFA) scheme.


Table 64: Summary of the electricity requirement at state level

Scenario

MUs

FY 16 (Base

Yr)

FY 17 FY 18 FY 19 FY 20 FY 21 FY 22 FY 23 FY 24 FY 25 FY 26 FY 27 FY 28 FY 29 FY 30 FY 31 FY 32

UR - Tr 84382 89223 89125 95528 100318 105543 109047 112788 118082 123548 130785 136371 142366 147331 153595 160163 168190

R - Tr 75169 75376 81479 86272 91480 95302 99387 104895 110622 118169 124144 130577 135924 142586 149624 158009

UR - Eco 84382 88242 88506 95191 100120 105404 110494 115792 122559 129641 138559 145718 153541 160404 168590 176932 186071

R - Eco 74522 75019 81372 86280 91535 96687 102120 108946 116148 125271 132724 140946 148134 156654 165414 174996

HW 74571 74791 80514 84614 88861 93240 97741 103335 109101 116548 122264 128426 133371 139464 145614 151893

CEA 69614 73222 76569 79485 83168 87051 91216 95782 101200 108808 117219 126290 161606

Table 65: Summary of peak demand at state level

Scenario

MW

FY 16

(Base Yr)


Load Factor

0.70 0.702 0.704 0.706 0.708 0.711 0.713 0.715 0.717 0.719 0.721 0.723 0.726 0.728 0.73 0.732 0.734

UR - Tr 11415 14509 14452 15446 16175 16946 17459 18007 18800 19616 20707 21532 22385 23102 24019 24977 26158

R - Tr 11415 12224 12222 13175 13910 14688 15258 15868 16701 17563 18710 19601 20532 21314 22297 23334 24574

UR - Eco 11415 14349 14352 15392 16143 16923 17691 18487 19513 20583 21938 23008 24143 25152 26364 27592 28939

R - Eco 11415 12118 12165 13157 13911 14696 15480 16304 17346 18441 19834 20956 22162 23228 24497 25796 27216

HW 11415 12126 12128 13019 13643 14267 14928 15605 16452 17322 18453 19304 20194 20913 21809 22708 23623

CEA 10961 11535 12070 12540 13133 13761 14435 15176 16048 17282 18651 20131 26575


9.2. Forecast at DISCOM level

9.2.1. JVVNL – Forecast of electricity consumption


Domestic UR-Tr 5,225 5,622 6,038 6,471 6,921 7,350 7,785 8,223 8,665 9,112 9,581 10,081 10,642 11,321 12,208 13,452

R-Tr 4,761 5,172 5,609 6,069 6,531 6,979 7,438 7,907 8,385 8,876 9,386 9,930 10,532 11,259 12,208 13,452

UR-Eco 5245 5630 6028 6440 6867 7309 7767 8243 8738 9253 9785 10339 10918 11523 12157 12830

R-Eco 4783 5188 5613 6059 6505 6969 7456 7966 8502 9064 9640 10255 10856 11487 12148 12830

UR-HW 5051 5402 5753 6105 6456 6808 7159 7510 7862 8213 8564 8916 9267 9619 9970 10321

CEA 4838 5271 5717 6148 6568 7000 7466 7946 8441 8973 9522 - - - - -

Non-Domestic

UR-Tr 2,597 2,757 2,925 3,103 3,289 3,479 3,679 3,889 4,109 4,339 4,573 4,817 5,072 5,337 5,612 5,897

R-Tr 2,092 2,248 2,415 2,592 2,780 2,975 3,181 3,400 3,631 3,876 4,130 4,397 4,678 4,973 5,283 5,608

UR-Eco 2602 2826 3060 3304 3559 3818 4090 4373 4669 4979 5290 5615 5955 6309 6679 7093

R-Eco 2095 2303 2523 2756 3003 3259 3530 3816 4120 4441 4770 5117 5484 5871 6280 6737

UR-HW 2550 2721 2883 3042 3199 3354 3508 3661 3813 3964 4115 4266 4416 4565 4715 4864

CEA 2117 2275 2441 2642 2856 3084 3351 3636 3942 4296 4675 - - - - -

PSL Tr 184 189 195 199 204 200 196 191 185 178 169 159 147 133 118 102

Eco 207 226 246 267 289 313 338 364 393 423 456 492 530 572 617 667

HW 213 232 250 269 287 305 324 342 361 379 397 416 434 453 472 490

CEA 155 122 131 141 152 163 176 189 204 220 236 - - - - -

Agri (M+N+P)

UR-Tr 7,700 8,164 8,652 9,166 9,705 10,254 10,832 11,439 12,075 12,741 13,423 14,134 14,877 15,651 16,456 17,291

R-Tr 5,600 6,018 6,463 6,937 7,440 7,967 8,527 9,122 9,754 10,423 11,118 11,853 12,629 13,447 14,308 15,213

UR-Eco 7890 8548 9216 9892 10573 11231 11884 12528 13157 13765 14345 14888 15383 15819 16182 16497

R-Eco 5750 6312 6896 7498 8119 8734 9358 9989 10621 11249 11867 12465 13036 13566 14044 14490


UR-HW 8405 9063 9720 10378 11035 11692 12350 13007 13664 14321 14978 15635 16292 16949 17606 18263

CEA 6101 6366 6473 6861 7240 7580 7916 8268 8683 9102 9528 - - - - -

Agri (F) UR-Tr 632 546 472 409 354 279 220 173 136 107 74 51 35 24 16 11

R-Tr 444 390 342 299 263 210 168 134 107 85 59 41 29 20 14 10

UR-Eco 105 87 68 57 55 55 54 53 53 52 47 49 50 51 53 54

R-Eco 77 64 52 44 44 44 44 44 44 44 40 42 44 45 47 49

UR-HW 357 126 68 57 55 55 54 53 53 52 47 49 50 51 53 54


Industry (S + M)

Tr 1,068 1,101 1,136 1,171 1,207 1,248 1,290 1,333 1,378 1,424 1,471 1,520 1,571 1,622 1,676 1,731

Eco 1209 1242 1277 1316 1358 1404 1453 1505 1559 1614 1671 1728 1785 1843 1900 1955

HW 1219 1268 1322 1381 1445 1516 1591 1670 1754 1842 1933 2027 2124 2224 2326 2431

CEA 1107 1182 1261 1347 1438 1535 1639 1750 1869 1995 2130 - - - - -

Industry (L)

Tr 4,010 4,259 4,523 4,805 5,103 5,255 5,411 5,572 5,736 5,904 6,077 6,253 6,434 6,619 6,809 7,003

Eco 3946 4216 4494 4780 5073 5374 5683 5998 6320 6648 6982 7321 7667 8018 8374 8735

HW 3954 4238 4530 4829 5136 5450 5771 6099 6433 6773 7119 7471 7828 8190 8558 8930

CEA 4030 4303 4594 4905 5237 5591 5969 6373 6804 7264 7755 - - - - -

PWW Tr 560 594 630 668 710 735 760 786 813 840 867 895 923 951 980 1,008

Eco 571 618 668 724 784 851 925 1006 1095 1195 1306 1428 1565 1718 1889 2079

HW 561 597 631 664 696 728 759 791 822 853 884 915 945 976 1006 1037

CEA 585 642 703 782 875 980 1097 1233 1393 1580 1795 - - - - -

Mixed Load

Tr 177 182 188 193 198 204 209 214 220 226 231 236 242 248 253 259

Eco 206 233 256 277 298 318 338 358 379 401 423 447 471 498 525 552

HW 206 233 256 277 298 318 338 358 379 401 423 447 471 498 525 552


CEA 203 209 215 221 227 233 239 245 251 257 263 - - - - -

Total for Utilities

UR-Tr 22153 23415 24759 26185 27691 29005 30383 31820 33316 34870 36465 38146 39942 41906 44128 46754

5.7% 5.7% 5.8% 5.8% 4.7% 4.8% 4.7% 4.7% 4.7% 4.6% 4.6% 4.7% 4.9% 5.3% 6.0%

R-Tr 18897 20154 21499 22933 24436 25772 27180 28659 30208 31831 33508 35284 37184 39273 41649 44386

6.7% 6.7% 6.7% 6.6% 5.5% 5.5% 5.4% 5.4% 5.4% 5.3% 5.3% 5.4% 5.6% 6.0% 6.6%

UR-Eco 21982 23625 25313 27056 28857 30673 32531 34428 36362 38329 40304 42307 44325 46351 48376 50463

7.5% 7.1% 6.9% 6.7% 6.3% 6.1% 5.8% 5.6% 5.4% 5.2% 5.0% 4.8% 4.6% 4.4% 4.3%

R-Eco 18844 20402 22024 23720 25473 27265 29124 31046 33032 35079 37154 39295 41439 43618 45823 48095

8.3% 8.0% 7.7% 7.4% 7.0% 6.8% 6.6% 6.4% 6.2% 5.9% 5.8% 5.5% 5.3% 5.1% 5.0%

HW 19259 20618 22153 23749 25352 26992 28651 30330 32032 33759 35504 37279 39070 40892 42751 44574

7.1% 7.4% 7.2% 6.7% 6.5% 6.1% 5.9% 5.6% 5.4% 5.2% 5.0% 4.8% 4.7% 4.5% 4.3%

CEA* 19135 20370 21535 23045 24591 26166 27854 29642 31587 33687 35905 - - - - -

6.5% 5.7% 7.0% 6.7% 6.4% 6.5% 6.4% 6.6% 6.6% 6.6%


9.2.2. AVVNL – Forecast of electricity consumption


Domestic UR-Tr 3,788 4,046 4,307 4,566 4,819 5,079 5,319 5,531 5,703 5,823 5,935 5,970 5,913 5,751 5,476 5,086

R-Tr 3,361 3,631 3,908 4,189 4,469 4,761 5,039 5,295 5,517 5,691 5,860 5,954 5,913 5,751 5,476 5,086

UR-Eco 3900 4192 4494 4807 5131 5461 5803 6157 6524 6903 7297 7705 8128 8568 9024 9507

R-Eco 3457 3757 4073 4405 4753 5113 5491 5888 6303 6739 7197 7676 8128 8568 9024 9507

UR-HW 3782 4172 4458 4742 5028 5319 5613 5909 6209 6511 6816 7124 7434 7747 8062 8380

CEA 3404 3684 3975 4298 4616 4945 5346 5763 6194 6708 7240 - - - - -

Non-Domestic

UR-Tr 1,411 1,555 1,713 1,887 2,078 2,200 2,327 2,462 2,603 2,752 2,895 3,044 3,198 3,358 3,524 3,694

R-Tr 1,110 1,239 1,383 1,542 1,719 1,841 1,971 2,110 2,257 2,413 2,568 2,730 2,901 3,079 3,266 3,461

UR-Eco 1366 1510 1662 1823 1992 2138 2291 2451 2620 2797 2960 3129 3306 3490 3681 3957

R-Eco 1072 1200 1338 1485 1643 1784 1935 2095 2265 2446 2619 2800 2991 3193 3404 3699

UR-HW 1326 1393 1471 1550 1630 1710 1790 1870 1951 2032 2113 2194 2275 2356 2438 2519

CEA 1119 1241 1370 1524 1695 1878 2096 2329 2582 2879 3198 - - - - -

PSL Tr 90 98 107 117 127 132 137 141 144 146 146 143 139 132 122 110

Eco 92 81 90 99 109 119 130 142 155 170 185 202 220 240 263 290

HW 97 109 121 133 145 156 168 180 191 203 214 226 237 249 261 272

CEA 71 56 61 67 74 81 89 98 108 119 130 - - - - -

Agri (M+N+P)

UR-Tr 5,658 6,060 6,488 6,944 7,429 7,888 8,370 8,876 9,407 9,963 10,499 11,059 11,644 12,255 12,890 13,549

R-Tr 4,116 4,469 4,849 5,260 5,701 6,132 6,591 7,078 7,595 8,144 8,687 9,261 9,868 10,507 11,181 11,888

UR-Eco 5511 5921 6328 6759 7214 7645 8047 8455 8864 9285 9710 10134 10558 10977 11391 11867

R-Eco 3994 4350 4713 5101 5517 5922 6314 6719 7132 7564 8007 8459 8918 9382 9850 10380

UR-HW 5716 6378 6882 7385 7889 8392 8896 9399 9903 10406 10970 11473 11977 12480 12984 13487


CEA 5293 5592 5768 5966 6241 6539 6822 7099 7400 7725 8062 - - - - -

Agri (F) UR-Tr 1,260 1,107 978 869 776 695 626 565 511 464 422 384 350 320 292 267

R-Tr 917 816 731 658 595 541 493 450 413 379 349 322 297 274 253 234

UR-Eco 1333 1032 795 657 622 608 615 539 538 536 535 533 532 531 530 529

R-Eco 966 758 592 496 476 471 482 428 433 437 441 445 449 454 458 462

UR-HW 1333 1032 795 657 622 608 615 539 538 536 535 533 532 531 530 529


Industry (S + M)

Tr 1,147 1,208 1,272 1,341 1,413 1,480 1,551 1,625 1,702 1,783 1,866 1,953 2,044 2,138 2,237 2,340

Eco 1162 1229 1297 1367 1438 1510 1584 1659 1736 1815 1895 1976 2059 2144 2230 2318

HW 1151 1209 1266 1323 1381 1438 1495 1552 1610 1667 1724 1782 1839 1896 1953 2011

CEA 1159 1233 1313 1397 1486 1582 1683 1791 1906 2028 2158 - - - - -

Industry (L)

Tr 2,425 2,537 2,653 2,774 2,901 3,039 3,184 3,336 3,495 3,661 3,892 4,137 4,397 4,673 4,964 5,273

Eco 2665 2832 3039 3229 3413 3596 3780 3968 4161 4359 4602 4817 5043 5280 5532 5799

HW 2673 2844 3051 3237 3413 3582 3746 3906 4064 4220 4410 4562 4714 4865 5014 5164

CEA 2508 2713 2934 3173 3432 3712 4015 4342 4696 5079 5494 - - - - -

PWW Tr 506 535 565 596 629 648 666 684 702 718 728 736 742 745 747 745

Eco 532 574 617 662 709 759 812 869 931 998 1070 1149 1234 1328 1431 1544

HW 521 557 591 625 658 690 722 754 786 817 849 880 911 942 972 1003

CEA 507 538 574 611 652 694 739 787 839 894 952 - - - - -

Mixed Load

Tr 113 117 121 126 130 131 131 132 133 134 136 137 139 140 142 143

Eco 114 126 135 144 153 162 171 180 188 197 206 215 224 232 241 250

HW 114 126 135 144 153 162 171 180 188 197 206 215 224 232 241 250

CEA 116 121 127 133 140 147 154 162 170 179 188 - - - - -


Total for Utilities

UR-Tr 16398 17263 18205 19220 20301 21291 22311 23352 24400 25444 26519 27564 28566 29512 30394 31208

5.3% 5.5% 5.6% 5.6% 4.9% 4.8% 4.7% 4.5% 4.3% 4.2% 3.9% 3.6% 3.3% 3.0% 2.7%

R-Tr 13785 14650 15590 16602 17683 18704 19763 20851 21957 23070 24231 25374 26438 27440 28388 29281

6.3% 6.4% 6.5% 6.5% 5.8% 5.7% 5.5% 5.3% 5.1% 5.0% 4.7% 4.2% 3.8% 3.5% 3.1%

UR-Eco 16676 17498 18458 19546 20781 21998 23232 24421 25716 27060 28458 29860 31304 32791 34322 36060

4.9% 5.5% 5.9% 6.3% 5.9% 5.6% 5.1% 5.3% 5.2% 5.2% 4.9% 4.8% 4.8% 4.7% 5.1%

R-Eco 14054 14908 15893 16986 18210 19437 20700 21949 23305 24725 26221 27738 29267 30821 32432 34249

6.1% 6.6% 6.9% 7.2% 6.7% 6.5% 6.0% 6.2% 6.1% 6.1% 5.8% 5.5% 5.3% 5.2% 5.6%

HW 14100 15208 16156 17179 18300 19470 20666 21789 22997 24215 25549 26799 28015 29226 30450 31688

7.9% 6.2% 6.3% 6.5% 6.4% 6.1% 5.4% 5.5% 5.3% 5.5% 4.9% 4.5% 4.3% 4.2% 4.1%

CEA 14177 15178 16122 17170 18335 19578 20944 22372 23895 25610 27423 - - - - -

7.1% 6.2% 6.5% 6.8% 6.8% 7.0% 6.8% 6.8% 7.2% 7.1%


9.2.3. JdVVNL – Forecast of electricity consumption


Domestic UR-Tr 3,561 3,820 4,085 4,352 4,616 4,834 5,032 5,199 5,327 5,406 5,485 5,492 5,415 5,243 4,971 4,621

R-Tr 3,156 3,424 3,702 3,987 4,275 4,526 4,761 4,972 5,148 5,277 5,410 5,472 5,415 5,243 4,971 4,621

UR-Eco 3556 3834 4124 4428 4746 5079 5429 5796 6182 6590 7019 7473 7954 8463 9004 9579

R-Eco 3151 3436 3738 4057 4396 4755 5137 5542 5974 6433 6923 7445 7954 8463 9004 9579

UR-HW 3638 3923 4201 4475 4748 5020 5291 5561 5831 6101 6371 6641 6911 7181 7451 7721

CEA 3293 3593 3834 4117 4384 4659 4962 5273 5593 5944 6306 - - - - -

Non-Domestic

UR-Tr 1,361 1,473 1,594 1,725 1,865 2,013 2,172 2,342 2,523 2,717 2,968 3,240 3,534 3,851 4,194 4,570

R-Tr 1,068 1,171 1,283 1,405 1,538 1,681 1,835 2,002 2,182 2,376 2,626 2,899 3,197 3,523 3,878 4,272

UR-Eco 1346 1487 1635 1792 1957 2112 2274 2444 2622 2809 2988 3176 3372 3578 3793 4065

R-Eco 1056 1182 1316 1460 1614 1763 1921 2089 2267 2457 2644 2842 3051 3273 3507 3800

UR-HW 1304 1416 1529 1641 1753 1866 1978 2090 2203 2315 2428 2540 2652 2765 2877 2989

CEA 1161 1366 1609 1908 2258 2665 3147 3719 4397 5196 6135 - - - - -

PSL Tr 147 149 152 154 155 169 184 201 219 238 251 262 271 274 272 264

Eco 153 158 170 181 193 204 216 227 239 251 262 274 286 297 309 321

HW 153 166 178 190 202 214 226 238 250 263 275 287 299 311 323 335

CEA 118 87 98 110 124 139 156 175 197 221 249 - - - - -

Agri (M+N+P)

UR-Tr 12,138 13,404 14,797 16,330 18,016 18,913 19,846 20,816 21,822 22,865 23,814 24,787 25,785 26,804 27,844 29,592

R-Tr 8,796 9,847 11,019 12,324 13,776 14,652 15,573 16,542 17,560 18,628 19,639 20690 21,780 22,909 24,076 25,884

UR-Eco 12020 13177 14385 15644 16960 18318 19737 21219 22769 24388 26083 27856 29712 31657 33694 35854

R-Eco 8716 9681 10712 11807 12969 14191 15487 16863 18322 19869 21510 23251 25098 27057 29135 31361

UR-HW 11893 12873 13853 14832 15812 16792 17771 18751 19731 20711 21690 22670 23650 24629 25609 26589


CEA 10324 11350 12273 13327 14478 15827 17239 18742 20398 22211 24196 - - - - -

Agri (F) UR-Tr 1,616 1,446 1,295 1,163 1,045 961 887 822 763 710 663 619 579 543 509 372

R-Tr 1,171 1,062 965 877 799 745 696 653 614 579 546 517 489 464 440 325

UR-Eco 1255 1134 1064 1020 991 973 964 965 975 990 1008 1031 1056 1084 1115 1148

R-Eco 910 833 793 770 758 754 757 767 784 806 832 860 892 927 964 1004

UR-HW 1255 1134 1064 1020 991 973 964 965 975 989 1008 1031 1056 1084 1115 1148


Industry (S + M)

Tr 883 922 963 1,007 1,053 1,090 1,128 1,168 1,209 1,253 1,294 1,336 1,380 1,426 1,474 1,546

Eco 897 942 988 1036 1086 1138 1193 1250 1310 1373 1439 1508 1580 1656 1735 1819

HW 922 963 1005 1049 1094 1140 1187 1234 1282 1330 1380 1429 1479 1530 1581 1632

CEA 895 947 1001 1059 1121 1186 1255 1327 1404 1486 1572 - - - - -

Industry (L)

Tr 1,286 1,401 1,531 1,678 1,843 1,919 1,999 2,081 2,167 2,257 2,348 2,442 2,540 2,643 2,749 2,879

Eco 1459 1579 1685 1788 1895 2008 2131 2266 2415 2581 2549 2750 2977 3233 3522 3848

HW 1348 1417 1463 1498 1525 1547 1565 1580 1592 1602 1393 1394 1393 1391 1388 1384

CEA 1233 1285 1339 1396 1455 1516 1580 1647 1716 1789 1865 - - - - -

PWW Tr 835 875 917 959 1,002 1,018 1,032 1,045 1,056 1,066 1,073 1,078 1,081 1,082 1,080 1,057

Eco 858 906 953 999 1044 1089 1132 1175 1217 1258 1297 1336 1373 1409 1444 1476

HW 861 903 947 993 1039 1085 1132 1179 1226 1273 1321 1368 1416 1463 1511 1559

CEA 824 857 896 936 979 1021 1066 1114 1164 1216 1271 - - - - -

Mixed Load

Tr 358 366 373 380 387 392 397 401 406 410 414 418 422 425 429 432

Eco 370 396 414 439 461 479 487 505 525 544 565 591 609 634 656 675

HW 370 396 414 439 461 479 487 505 525 544 565 591 609 634 656 675

CEA 357 368 375 383 392 400 409 418 427 436 446 - - - - -


Total for Utilities

UR-Tr 22,185 23,856 25,707 27,746 29,983 31,309 32,677 34,075 35,494 36,922 38,309 39,675 41,007 42,292 43,521 45,332

7.5% 7.8% 7.9% 8.1% 4.4% 4.4% 4.3% 4.2% 4.0% 3.8% 3.6% 3.4% 3.1% 2.9% 4.2%

R-Tr 17,700 19,217 20904 22,771 24,831 26,191 27,605 29,065 30,562 32,084 33,600 35,114 36,576 37,990 39,369 41,279

8.6% 8.8% 8.9% 9.0% 5.5% 5.4% 5.3% 5.2% 5.0% 4.7% 4.5% 4.2% 3.9% 3.6% 4.9%

UR-Eco 21918 23613 25418 27327 29332 31401 33563 35848 38253 40783 43212 45995 48920 52011 55271 58784

7.7% 7.6% 7.5% 7.3% 7.1% 6.9% 6.8% 6.7% 6.6% 6.0% 6.4% 6.4% 6.3% 6.3% 6.4%

R-Eco 17570 19113 20768 22537 24415 26382 28461 30685 33053 35572 38022 40857 43820 46949 50276 53882

8.8% 8.7% 8.5% 8.3% 8.1% 7.9% 7.8% 7.7% 7.6% 6.9% 7.5% 7.3% 7.1% 7.1% 7.2%

HW 17260 18552 19851 21161 22472 23997 25529 27094 28683 30292 31723 33389 35034 36686 38359 39979

7.5% 7.0% 6.6% 6.2% 6.8% 6.4% 6.1% 5.9% 5.6% 4.7% 5.3% 4.9% 4.7% 4.6% 4.2%

CEA 18204 19853 21425 23236 25190 27413 29813 32415 35296 38499 42038 - - - - -

9.1% 7.9% 8.5% 8.4% 8.8% 8.8% 8.7% 8.9% 9.1% 9.2%


10. Functional strategies for supply side to meet the projected demand

The demand forecasted using alternate approach in the previous section have to be met by ramp up of supply

sources or by planned procurement plans. While the Utilities currently have tied up capacities to meet the

electricity and peak demand requirement, the same may not be sufficient to meet the future demand.

10.1. Existing supply position in Rajasthan

Power sector in Rajasthan has witnessed substantial growth over the past decade due to increase in generation

capacity and strengthening of network infrastructure. These initiative have led to an improvement in the overall

power supply position of the state. Currently, most connected consumers in the state are being provided with at

least 21-22 hours of power supply24.

In terms of generation capacity, the total power generation installed capacity has grown from 10,160 MW to

18,826 MW during April 2012 to February 2017. The share of the generation in terms of MW and MUs is given

in the following:

Figure 56: Share of supply sources as of FY17

The table below provides the breakup of the sources25 as of February 2017:

Figure 57: Contracted capacity of Rajasthan as of February FY17 (MW)

Sl. No. Source Central sector State sector Private Total

1. Coal 1015 5190 3196 9401

2. Hydro 742 1088 - 1829

3. Gas 221 604 - 825

4. Nuclear 573 - - 573

5. Renewable 775 165 5258 6198

Total 3326 7047 8454 18826

It can be observed (Figure 1) that nearly 72% of the state demand (in MUs) is met through supply from coal

based sources. Even though the state has a high potential for renewables and current installed capacity (FY17)

24 Rajasthan Power for All document 25 State Renewable Energy Action Plan

54%

4%2%

10%

6%

22%

1%

Contracted capacity (MW)

Coal

Gas

Nuclear

Hydro

Solar

Wind

Others72%

5%

4%

9%

1%8% 1%

Supply met from sources (MU)

Coal

Gas

Nuclear

Hydro

Solar

Wind

Others


for renewables is about 29% (in MWs), contribution of renewable energy towards meeting electricity requirement

of the state is a meagre 9%.

In terms of renewable energy (RE) potential, Rajasthan has solar potential of 142 GW26 and wind potential of

18.7 GW27. The state receives maximum solar radiation intensity in India with very low average rainfall. It also

has uncultivated land in abundance and favorable climate, which has led to the state emerging as the leading

hub for renewable energy. RE capacity addition in state has grown at yearly rate of 22 %, which is more than

national average of 14%28. At present, installed RE capacity in the state is around 6.2 GW, of which wind energy

capacity is ~70%. However, solar is expected to overtake and lead the renewable energy generation in the state

in future.

10.2. Options for Supply side evaluation

The following two options of demand side results have been considered for evaluating the supply side option.

Scenario 1: Demand derived from restricted condition – result using trend method

Scenario 2: Demand derived from unrestricted condition – result using econometric method

The following table summarizes the two scenarios which have been considered.

Table 66: Demand projections for the State (in MUs) considered for supply side evaluation


Scenario 1 –

Restricted (R) 55383 57634 61931 66609 71720 75845 80265 84891

Scenario 2 –

Unrestricted (UR) 65015 67673 72353 77301 82636 87935 93513 99186


Scenario 1 –

Restricted (R) 89706 96011 101052 106476 111024 116655 122604 129568

Scenario 2 –

Unrestricted (UR) 105129 112577 118613 125202 131020 137930 144980 152578

10.3. Assessment of supply capacity required to meet projected demand for the state

Supply side planning is a critical task which leads to meeting of the ever increasing demand of power in any

region or state. The planning of capacity to be required in future is crucial as it involves capital investment and

also substantial period of time before it can be used for generation. Historically, the capacity has been dependent

on coal, but moving ahead, the same may not be the case. Since resources are limited, it is essential that planning

is optimized.

Broad assumptions considered for the current evaluation are:

Assessment of supply side is for the consumer demand at state level to be met by the Distribution utilities.

26 NISE Estimate 27 NIWE estimate 28 State Renewable energy action plan


All other additional demand from e.g. electric vehicles, latent demand, demand from new housing and

infrastructure, metro, industrial corridor etc. has been assumed to be met by state distribution utilities

Evaluation is based on the assumption that electricity from coal based stations will be least priority i.e.

available capacities from other sources will be considered first to meet the demand. The same is in line

with the goal of reducing emissions and reducing dependence on coal based sources

The planning is as per the current policy decision wherein renewable capacity addition has been given

impetus by the Government

Renewable sources – Solar, Wind, Biomass etc. have been considered as must run

Electricity scheduled from available/ new hydro and gas based stations will be considered before coal

based sources

Generation from rooftop solar plants has been considered and has been given preference in consumption

by consumers to meet their own demand first.

For meeting peak demand, availability from various generation sources have been considered. The

peaking availability has been considered as per data given in the final NEP’2018.

Impact of pricing of power supply has not been considered

10.3.1. Electricity requirement of state consumers to be met by sources other than Distribution utilities

10.3.1.1. Demand from Open Access, Captive and Railways

Electricity requirement for open access consumers as well as for captive and railways are not met by the

distribution utilities. Hence, the projected demand for the three heads have been deducted from the overall state

demand. The following table provides the quantum of projected demand to be subtracted from the overall state

demand.

Table 67: Electricity demand from Open Access, Captive and Railways

MUs FY 17 FY 18 FY 19 FY 20 FY 21 FY 22 FY 23 FY 24

OA & captive 4568 2845 2987 3136 3293 3458 3630 3812

Railways 433 451 470 488 507 526 544 563


OA & captive 4003 4203 4413 4633 4865 5108 5364 5632

Railways 581 600 618 637 656 674 693 711

10.3.1.2. Demand to be met from rooftop solar plants

India has set a path to achieve 100 GW installed capacity through grid-connected solar energy, out of which

40 GW is targeted to come through rooftop solar installations by 2022. Till date, efforts have been put in place to

develop the rooftop solar photovoltaic sector in India by the government, regulatory commissions and concerned

agencies. Basic framework exists in the country and implementation of rooftop solar power plants has been

ongoing. However, considering the targets committed with respect to rooftop solar photovoltaic plants, there is

still a high gap to be met in terms of installed capacity.


In February 2015, the regulations for net metering for rooftop solar system was issued by the Rajasthan Electricity

Regulatory Commission, under which, an individual could use the power generated and the surplus could be fed

into the DISCOM's grid. In October’17, Rajasthan Renewable Energy Corporation Ltd (RRECL) came out with

tender for 18 MW of grid-connected rooftop solar projects under the RREC’s rooftop power generation program

for 2017-18. As per India Solar Rooftop map29, 2017, the total installed rooftop capacity in Rajasthan has been

reported at 129 MW. The capacity includes that of industrial, commercial, residential and public sector

installations in the state.

The report also highlights that between FY13 to FY17, the all India market for rooftop solar has grown at a CAGR

of 66% mainly due to sudden surge in installations and low installed base capacity till FY13-14. However, in terms

of installed capacity, ~ 10.8GW is expected at an all India basis by FY21. Target for FY22 for rooftop solar is 40

GW. For Rajasthan, MNRE has given a target of 2300 MW upto FY22.

To project the solar rooftop capacity in the state, the following data/ assumptions have been considered:

Estimated installed capacity from FY19 to FY22 has been assumed to grow at 66% in line with that of

the country. The assumption is also based on the fact that Rajasthan is one of the leading states in solar

installation and since the state has been given a target of 2300MW, the period upto FY22 will involve

major installations.

Post FY22, a nominal growth rate of 15% upto FY32 has been considered

Capacity Utilization Factor of 19% and auxiliary consumption of 0.25%, as per CERC RE Regulations,

2017 and 300 days of solar availability has been considered

The following tables provides the projected installed capacity and electricity to be generated in MUs.

Table 68: Expected consumer demand to be met by rooftop solar


MW 52 129 214 355 590 980 1126 1295

MUs 71 176 292 485 805 1337 1537 1768


MW 1490 1713 1970 2266 2606 2996 3446 3963

MUs 2033 2338 2689 3092 3556 4089 4702 5408

The energy projections (in MU) have been assumed to be used by respective prosumers to meet the self-demand.

Hence the same will not be met by Distribution utilities and will be subtracted from the total state demand

projections along with Open Access, Captive, and Railways etc. The balance available demand under both the

scenarios is the net electricity requirement which will have to be served by the distribution utilities in the state.

10.3.2. Electricity requirement to be met by Distribution utilities in the state

The net addressable electricity demand has been arrived as per the following formula:

Net addressable electricity demand at consumer end to be met by distribution utilities

= Total projected electricity demand – (electricity demand met by OA, Captive, Railways, rooftop solar)

29 India Solar Rooptop map, Bridge to India, September 2017


Table 69: Net addressable electricity demand at consumer end to be met by Distribution utilities


Scenario 1-R 50311 54162 58182 62499 67115 70525 74553 78749

Scenario 2-UR 59943 64201 68604 73191 78031 82615 87801 93044


Scenario 1-R 83089 88871 93332 98114 101948 106784 111845 117817

Scenario 2-UR 98512 105437 110893 116840 121944 128059 134221 140827

To meet the net addressable demand at the consumer end, the distribution utilities will have to enter into

agreements with the available generation sources. The addressable demand required at the state boundary is

arrived by grossing up the demand at the consumer end with the overall T&D losses

Table 70: Net addressable electricity demand at state boundary to be procured by Distribution utilities


Scenario 1-R 68286 70836 76547 80950 85606 88618 92314 97305

Scenario 2-UR 81359 83965 90259 94798 99530 103810 108719 114968


Scenario 1-R 102462 109381 114660 120322 124812 130520 136494 143680

Scenario 2-UR 121481 129770 136234 143286 149292 156524 163802 171741

The projection of the balance generation sources is given in the following section.

10.3.3. Projection of generation sources to meet projected electricity demand

10.3.3.1. Wind power

The installed wind power capacity has grown at a CAGR of 8% during the period FY13 to FY18 as per actual

data. As of FY18, the reported wind power installed capacity is 4295 MW. CAGR rate of 8% has been used to

project the installed capacity for forecast period.

To arrive at the energy in MUs to be generated, an average CUF of 22% has been considered as per CERC RE

Regulations 2017.

10.3.3.2. Solar

Installation of solar capacity has witnessed a 5 year CAGR rate of 32% for the period from FY13 to FY18. While

the higher rate is because of low base in FY13 and a high growth achieved in the period of FY16-18. The

momentum is expected to continue upto FY 22 and hence the same CAGR rate has been considered for

projections upto FY22. As per planning estimates30, the installed capacity is expected to reach 4049 MW by FY22

against a target of 5762 MW.

30 RRVPNL data accessed


Projected energy which can be met from solar sources has been arrived at by considered a CUF of 19% CERC

RE Regulations 2017, 30o days of solar availability and a deration of 0.5%.

10.3.3.3. Biomass

Biomass capacity in FY15 in Rajasthan has been reported at 75 MW and as of FY18, the installed capacity is

120MW. In terms of actual electricity procured by the utilities, 279 MU were procured in FY15 and it increased to

293 MU in FY18. Even though there has been an increase in capacity (in MW), the energy procured (in MU) has

not increased at the same pace indicating a decrease in net PLF to 32% in FY 18. As per the State Renewable

Energy plan, projects of 18.8MW are in pipeline. YoY capacity addition of 20MW has been considered upto FY

22. For period post FY22, a CAGR of 15% (which is the CAGR for period FY15 to FY22) has been assumed to

continue for the remaining years. To project the electricity generated, a YoY improvement in PLF of 1% has been

taken with assumption that there will be better fuel availability and intake by utilities.

10.3.3.4. Hydro

Growth in installed capacity for hydro power stations tied up to Rajasthan has grown at a CAGR of 6% in the

period from FY13 to FY17. It has been assumed that the same growth will continue. In addition expected capacity

of 230 MW (share of Rajasthan) from Parbati II, Tehri Stage II and Teesta VI hydro power plants have also been

added in year FY2231.

Even though country has hydro power potential, the growth in installed capacity has lagged in comparison to

other generation sources. The electricity procured from hydro power stations in the previous five years indicates

a net PLF in range of 30-35% and the electricity consumption has increased at a rate of 6.2% upto FY17. For the

future projections, the present condition has been extrapolated assuming the same growth rate of 6.2%.

10.3.3.5. Nuclear

The capacity linked to Rajasthan as of FY18 is 557 MW and actual electricity procured is 2971 MU. Two new

installations (Unit VII and Unit VIII) as part of expansion of existing Rajasthan Atomic Power Project are expected

to be commissioned in year FY19 and FY20. The expected electricity to be generated has been derived using

the actual average PLF observed for the previous years.

10.3.3.6. Gas and Coal

For gas and coal, two scenarios have been considered

Planning scenario – Gas based stations has been accorded a higher priority than coal based stations to

derive the additional coal capacities which will be required. For gas, the committed capacity additions upto

FY22 as per final NEP’18 has been considered. Also, as per the results of the final NEP’18, there would be

no capacity additions from FY22 onwards. Starting from FY17, a PLF of 21% has been considered which is

expected to remain constant till FY32. In case availability of gas improves, the PLF is expected to go up.

Merit order scenario – In case merit order is considered, gas will be accorded the least priority. Since

availability of gas is a concern, the unfilled load due to low PLF of gas based stations may have to met from

additional coal based capacities in future.

Thermal capacity using coal for Rajasthan was 10226 MW as of FY 17 including share from central stations and

for plants managed by Rajasthan Vidyut Utpadan Nigam (RVUN). For the period of FY 13 to FY 17, the CAGR

growth in capacity is 14%. For the period from FY19 to FY as per CEA32 data, the capacity addition for Rajasthan

is expected to be 1386 MW in FY19, 66 MW in FY20 and 500MW in FY21. Post the capacity additions, no

increase in capacity has been estimated for the period from FY22 to FY27 for the state.

31 91st CEA quarterly review, Jan’18 32 Broad status of thermal power projects in the country, CEA, Dec’17


For the period from FY14 to FY17, based on the actual MW capacity and electricity procured from thermal power

stations, a decreasing trend of average PLF has been observed (auxiliary consumption considered as 6.5% as

per final NEP’1833).

The decrease in trend for coal based

stations may be attributed to excess

capacity and due to subdued demand

coupled with higher penetration of

renewable energy. As per the final NEP’18,

112% achievement has been achieved

against the target set for the period FY

2012-17. Specifically for Rajasthan, where

nearly 29% of capacity (in terms of MW) is

renewable based, the decrease in PLF in

the year from FY16 to FY17 is higher.

For a renewable rich state like Rajasthan,

the trend is expected to further decrease in

case the momentum of RE addition

continues and if the system is able to accommodate the increasing energy generated from RE sources in the

state.

Since coal has been given the least priority in planning, the distribution utility in the state has to meet the balance

electricity demand of consumer by sourcing electricity generated from the installed coal based capacities. Since

the PLF of coal based power plants is already decreasing, the focus in planning will be to utilize the already

installed capacity to meet the increasing demand.

Depending on the results of the scenarios used, the requirement of coal based capacity to meet the electricity

demand will be different.

10.3.3.7. Retirement of old units

The retirement of old units has been considered as per the data provided in the final NEP’2018. At an all India

level, it is estimated that by the year 2022, coal capacities of 22,716MW will be retired, 5927 MW of old units and

16789 MW will complete 25 years by 2022 and won’t meet the new environmental norms. It has also been

estimated that 25,572MW of coal capacities will be retired by the year 2027.

The capacities linked with Rajasthan which will retire by the year 2022 and 2027 were worked out from the data

given. The following capacities are expected to be retired for the state of Rajasthan

Table 71: Expected capacities to be retired by 2022 and 2027

Retiring capacities Upto 2022 Upto 2027

CEA Rajasthan CEA Rajasthan

Retirement due to age 5927 0 0 0

Due to environmental norms - Coal 16789 850 25572 1378

The expected capacities to be retired by the year 2032 has been projected using the data available for 2022 and

2027 and it is expected that approximately 2234 MW of capacities will be retired for Rajasthan. The projection is

based on the assumption that there will not any change in the current norms. The following section provides an

estimate of the coal based capacities to be required in order to meet the electricity requirement.

33 CEA Final NEP’18, Page 142

72.70%71.51%

68.78%

58.65%

65.55%64.46%

62.28%

59.88%

50%

55%

60%

65%

70%

75%

FY14 FY15 FY16 FY17

PLF (derived) for Rajasthan CEA - All India (NEP'18)

Figure 58: Trend of PLF for coal based thermal power stations


Coal capacities required to meet electricity consumption demand

The summary of generation capacities and the electricity to be generated from all the sources is given in the table below:

Table 72: Summary of projections - generation sources


Wind

MW 4124 4295 4650 5035 5452 5592 6055 6557 7100 7687 8324 9013 9759 10567 11442 12389

MUs 7948 8360 9142 9997 10934 11327 12387 13548 14817 16202 17720 19379 21193 23177 25347 27719

Solar

MW 1194 1784 2358 3116 3519 4049 4657 5355 6159 7083 8145 9367 10772 12387 14246 16382

MUs 1987 2999 4004 5343 6095 7083 8228 9556 11100 12893 14975 17394 20203 23464 27255 31655

Biomass

MW 102 120 138 158 178 198 227 261 300 345 396 455 523 600 690 792

MUs 250 300 352 411 473 536 627 735 862 1011 1184 1388 1627 1904 2233 2615

Hydro

MW 1931 2047 2171 2302 2441 2818 2988 3168 3359 3561 3776 4004 4245 4501 4772 5060

MUs 5582 5917 6276 6655 7056 8146 8638 9158 9710 10294 10916 11575 12271 13011 13795 14627

Nuclear

MW 557 557 1257 1957 1957 1957 1957 1957 1957 1957 1957 1957 1957 1957 1957 1957

MUs 3123 3123 7047 10972 10972 10972 10972 10972 10972 10972 10972 10972 10972 10972 10972 10972

Gas

MW 825 825 825 825 825 838 838 838 838 838 838 838 838 838 838 838

MUs 1517 1517 1517 1517 1517 1541 1541 1541 1541 1541 1541 1541 1541 1541 1541 1541

Total

MW 8733 9628 11399 13393 14372 15452 16722 18136 19713 21471 23436 25634 28094 30850 33945 37418

MUs 20407 22216 28338 34895 37047 39605 42393 45510 49002 52913 57308 62249 67807 74069 81143 89129


Of the net addressable electricity requirement at state boundary, the MUs to be met by all the generation sources

(other than coal) is given as above. The balance electricity requirement is to be met from based thermal power

stations.

Table 73: Balance electricity demand to be met by coal based sources

MUs FY 17 FY18 FY19 FY20 FY21 FY22 FY23 FY24

Scenario 1-R 47879 48620 48209 46055 48559 49013 49921 51795

Scenario 2-UR 60952 61749 61921 59903 62483 64205 66326 69458

FY 25 FY26 FY27 FY28 FY29 FY30 FY31 FY32

Scenario 1-R 53460 56468 57352 58073 57005 56451 55351 54551

Scenario 2-UR 72479 76857 78926 81037 81485 82455 82659 82612

To derive the coal capacities, a PLF of 70% has been considered for the year FY17 and a YoY progressive

increase of 0.75% has been assumed.

Table 74: Coal capacities required to meet net addressable electricity demand

MW FY 17 FY18 FY19 FY20 FY21 FY22 FY23 FY24

Scenario 1-R 7808 7870 7745 7344 7686 7700 7784 8016

Scenario 2-UR 9940 9995 9948 9552 9890 10086 10342 10750

FY 25 FY26 FY27 FY28 FY29 FY30 FY31 FY32

Scenario 1-R 8212 8610 8680 8723 8499 8354 8130 7953

Scenario 2-UR 11134 11719 11945 12173 12149 12202 12141 12044

In the results arrived at for both the scenarios, the requirement for meeting peak demand has not been included.

The following section looks into the capacity required to meet the peak demand in the state.

10.3.4. Projection of generation source to meet projected peak demand

Distribution utilities have to plan for meeting the peak demand which may be incident on the system. The planning

for peak demand is critical as otherwise there will be load curtailments or blackouts or the grid system may also

fail in extreme circumstances. Hence, the system has to be designed keeping in view the future network

requirements and the utilities should plan the generation sources so that peak demand can be met, other than

meeting the electricity demand. To arrive at the additional capacity required to meet the peak demand, capacity

of only conventional sources have been considered.


Peaking availability

To arrive at the conventional capacity required, the peaking availabilities of the generating units have been

considered as per the data provided in the final NEP’18.

Table 75: Peaking availability of the generating units

Sl. No. Generation source Peaking availability

1. Thermal 85%

2. Gas 88%

3. Hydro 68%

4. Nuclear 87.5%

Spinning reserve

In addition, to peaking availability of the generating units, spinning reserve is also considered. As per the final

NEP’18, the electricity policy stipulates that in addition to enhancing the overall availability of installed capacity

to 85%, a spinning reserve of at least 5% at national level is required to be created to ensure grid security, quality

and reliability of power supply. However, in the present scenario, since the planning is being undertaken at the

state level, additional 5% for spinning reserve has not been considered.

PLFs of generation sources considered to meet peak

In order to meet the peak demand, the PLFs for various generation sources have been considered. The PLFs

have been considered based on the quantum of peak demand that may be met by the sources. The details of

PLF for various generation sources and the quantum of peak demand met by the sources are given in the

following table:


Table 76: PLFs of generation sources considered to meet peak demand

Table 77: Quantum of peak demand met by various generation sources other than coal

The balance of the net addressable peak demand has to be met by the coal based generation sources.

MW FY17 FY18 FY19 FY20 FY21 FY22 FY23 FY24 FY25 FY26 FY27 FY28 FY29 FY30 FY31 FY32

Wind 11% 11% 11% 11% 11% 12% 12% 12% 12% 12% 12% 12% 12% 13% 13% 13%

Solar 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0%

Biomass 50% 50% 50% 50% 50% 50% 50% 50% 50% 50% 50% 50% 50% 50% 50% 50%

Hydro 80% 80% 80% 80% 80% 80% 80% 80% 80% 80% 80% 80% 80% 80% 80% 80%

Gas 21% 21% 21% 21% 21% 21% 21% 21% 21% 21% 21% 21% 21% 21% 21% 21%

Nuclear 80% 80% 80% 80% 80% 80% 80% 80% 80% 80% 80% 80% 80% 80% 80% 80%


Wind 454 477 522 571 624 646 707 773 846 925 1011 1106 1210 1323 1447 1582

Solar 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

Biomass 51 60 69 79 89 99 114 131 150 173 198 228 262 300 345 396

Hydro 1545 1638 1737 1842 1953 2254 2390 2534 2687 2849 3021 3203 3396 3601 3818 4048

Gas 173 173 173 173 173 176 176 176 176 176 176 176 176 176 176 176

Nuclear 446 446 1006 1566 1566 1566 1566 1566 1566 1566 1566 1566 1566 1566 1566 1566

Total 2669 2794 3507 4231 4405 4741 4953 5180 5425 5689 5972 6279 6610 6966 7352 7768



Total peak demand for the state

Scenario 1-R 12224 12222 13175 13910 14688 15258 15868 16701 17563 18710 19601 20532 21314 22297 23334 24574

Scenario 2-UR 14349 14352 15392 16143 16923 17691 18487 19513 20583 21938 23008 24143 25152 26364 27592 28939

Peak demand for Railways, OA, Rooftop etc.

825 563 606 663 739 852 912 978 1051 1131 1219 1315 1423 1544 1678 1828

Balance net addressable peak demand to be met by the state DISCOMs

Scenario 1-R 11399 11659 12569 13247 13949 14406 14956 15723 16512 17579 18382 19217 19891 20753 21656 22746

Scenario 2-UR 13524 13789 14786 15480 16184 16839 17575 18535 19532 20807 21789 22828 23729 24820 25914 27111

Balance peak demand to be met by coal based sources (after considering the peak demand met by other generation sources). The coal capacity derived to meet peak demand

shall also be used to meet the electricity demand for coal. The derived PLF also given in the following

Scenario 1-R 8730 8865 9062 9016 9544 9665 10003 10543 11087 11890 12410 12938 13281 13787 14304 14978

Derived PLF 63% 63% 61% 58% 58% 58% 57% 56% 55% 54% 53% 51% 49% 47% 44% 42%

Scenario 2-UR 10855 10995 11279 11249 11779 12098 12622 13355 14107 15118 15817 16549 17119 17854 18562 19343

Derived PLF 64% 64% 63% 61% 61% 61% 60% 59% 59% 58% 57% 56% 54% 53% 51% 49%

In case 85% peaking availability (as per Final NEP’18) is also considered in the above, the derived coal capacity is given below:

Scenario 1-R 10271 10429 10661 10607 11228 11371 11768 12404 13044 13989 14600 15221 15624 16220 16828 17622

Derived PLF 53% 53% 52% 50% 49% 49% 48% 48% 47% 46% 45% 44% 42% 40% 38% 35%

Scenario 2-UR 12771 12935 13269 13235 13857 14233 14849 15712 16597 17786 18608 19470 20140 21005 21838 22757

Derived PLF 54% 54% 53% 52% 51% 51% 51% 50% 50% 49% 48% 48% 46% 45% 43% 41%


In such a scenario wherein the PLFs are below 55%, there is a need to explore other peaking power sources than depending on coal based capacities only to meet the

peak demand.

Based on the above results, the coal capacity required to meet peak demand, being the higher capacity, has been considered as the final capacity required for the state.

MW FY 17 FY 18 FY 19 FY 20 FY 21 FY 22 FY 23 FY 24 FY 25 FY 26 FY 27 FY 28 FY 29 FY 30 FY 31 FY 32

Tied up

capacity 10226 10905 12291 12357 12857 12007 12007 12007 12007 12007 11604 11974 12082 12323 12527 12825

(Deficit)/ Excess of coal capacities

Scenario 1 – R -45 476 1630 1750 1629 636 239 -397 -1037 -1982 -2996 -3247 -3542 -3897 -4301 -4797

Scenario 2 –UR -2545 -2030 -978 -878 -1000 -2226 -2842 -3705 -4590 -5779 -7004 -7496 -8058 -8682 -9311 -9932

The present planning of tied up capacities for the state is based on non-consideration of baseline correction. In view of the same, a gap in supply side requirement is

observed. Over the forecast period, the gap is increasing and will impact the planning of reserve capacities in the state. The shortage of tied up capacities will also impact

the reliability of power supply in the state in the long term. Going ahead, all demand forecasting exercises should incorporate baseline correction of data in order to address

the gap in capacities while planning the supply side generation sources.


10.4. Projected supply position in Rajasthan

The expected change in installed capacities of generation sources, in case current policies continue, is

highlighted in the following figure. From a current scenario wherein installed capacity of coal based power plants

constitute 54% and 29% from renewables, by the year FY32 RE may constitute up to 55% and share of coal will

be 30% and balance will be met from hydro, gas and nuclear based power stations. Major installed capacity is

expected to be from solar in the state.

Figure 59: Scenario 1: Projected supply position (MW) and change in share of generation sources

However, coal will still be the dominant generation source in meeting the electricity demand of consumers.

Currently as of FY17, with 54% share of installed capacity, electricity from coal based plants contributes upto

71% of the total requirements and same is expected to decrease up to 46% as of FY32.

Figure 60: Scenario 1: Projected supply position (MUs) and electricity to be supplied by generation sources

For Scenario 2, the share of coal based capacity will decrease to 35% and renewables is expected to constitute

51% by FY32. The higher coal share than scenario 1 is due to higher requirement of coal based stations to meet

the peak demand. The share of electricity from generation sources in this scenario will remain the same i.e. 46%

from coal and 37% from renewable sources as of FY32.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%


Wind Solar Solar rooftop Biomass Hydro Gas Nuclear Coal

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%


Wind Solar Solar rooftop Biomass Hydro Gas Nuclear Coal


10.5. Functional strategies for managing higher integration of renewables

10.5.1. Managing the load curve

The increase in contribution of renewables will also bring with it more variability and uncertainty and the overall

systems will require a change in order to effectively integrate the same. Electricity generation from renewables

will vary over time in a day and over seasons and it is difficult to predict a specific trend unless short term demand

forecasting is undertaken using advanced methods. While generation in solar is expected to mostly follow a fixed

pattern in the day time, seasonal variation in it brings uncertainty and variability e.g. in monsoons. Similarly,

generation from wind follows seasonal pattern - maximum during monsoon and having day wise variability also.

Also generation from solar is maximum during a time when the load requirement is not maximum, hence to use

the generated electricity, power from other sources will have to be curtailed which brings in more balancing and

ramping requirements. Generation from rooftop also is more localized and may involve bi-directional flow in

comparison to conventional sources which requires planning of dispatch and availability of transmission

networks.

Hence integration of renewables will bring with it greater challenges which have to be addressed in order to utilize

the capacities in an optimized manner. This will require higher management of generation sources at the block

levels in a day.

The following section will look into the possible scenarios of how the daily demand can be managed in event of

a higher integration of renewables.

The typical demand profile in the state of Rajasthan and the average generation profile of solar and wind is as

indicated in the figures below. Hourly generation is average generation profile of wind and solar. To calculate Net

Load profile, RE generation is deducted from state’s total demand profile.

Figure 61: Typical load curve in a day - Rajasthan

4000

5000

6000

7000

8000

9000

10000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

MW

dem

and

Hours in a day

Total Demand - Rajasthan Net Load (Total minus RE)

Demand met by own generation - Rajasthan


Figure 62: Average generation profile of wind and solar in a day In future years also, the state profile is

expected to remain similar. From the

profile, it can be seen that there are two

peaks in a day which are to be

addressed.

The state has implemented a rooster

system for supplying to agriculture (more

than ~40% of the state demand) and for

other services (e.g. public water works).

It also follows a loss based supply

management in order to better address

the peak demand. Hence, the morning

peak (8-9 am) is mostly due to demand

from domestic consumers, industrial (e.g. start of production) and from commercial segments. The demand

decreases and again reaches a peak around 7-9 pm. This variation in the daily load curve requires ramping and

balancing requirements. In the present scenario, since share of renewables is lesser (~10% in MU terms), the

ramping and balancing requirement is also less. However, with renewable contribution expected to reach ~32%

by FY32, the requirements will be much higher.

In order to serve the variation in the load curve, the net demand, post usage of all renewable sources, shall be

served by the conventional generation sources.

The following figure provides an illustration of probable curve and net demand curve (higher renewables).

The requirement of ramping and balancing will depend on where the base load (to be met by coal and nuclear

sources) is fixed.

Case A

In the present case, the base load is considered near to the average load of the net demand curve. In this

scenario, the complete base load requirements will be met from coal and nuclear sources and other variation will

be met from hydro, gas, renewables and other short term market instruments. For a high renewable scenario,

most of the renewable generation during the day will have to be either exported or additional storage requirements

will have to be developed. In case of storage availability, electricity generated during the day can be stored and

utilized to meet the demand of post afternoon and evening.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

MW

dem

and

0

200

400

600

800

1000

1200

1 2 3 4 5 6 7 8 9 101112131415161718192021222324

Wind Solar

Case A

Case B

Case C


Requirements

Ramping Lower ramping up/ down will be required from coal based stations. Nuclear is more inflexible,

hence more suitable for lesser variation.

Balancing Balancing requirement from gas and hydro stations will be less. More in line with lower growth

in installations of gas and hydro stations in the country

Storage High storage requirement to utilize electricity from renewables. Otherwise excess renewable

generation will have to exported to other states or to be utilized using short term market

Case B

Base load is considered as shown in the above illustration and it is planned to utilize higher renewables so as to

meet the state demand than case A. This will involve lower quantum of export and facilities required for storage

will also be lesser than case A. In case the cost of availing electricity through storage remains high, to meet up

for the additional requirement during morning and evening, there will be requirement of flexible generators such

as reservoir hydropower plants, gas based generating plants which could respond and adjust to the demand-

supply fluctuations in a short time frame.

Requirements

Ramping Medium ramping requirement for coal based stations.

Ramp up – to meet morning peak

Ramp down – to accommodate renewable generation

Ramp up – to meet evening peak in case of adequate balancing and storage requirements are

not developed.

Balancing Capacity of hydro and availability of gas based stations will have to be improved in this case to

meet the increased ramping requirements in shorter time period.

Storage Storage requirement to utilize electricity generated from renewables post evening and to reduce

variability.

Case C

Base load to accommodate the maximum renewable energy consumption in the state.

Requirements

Ramping Higher ramping requirement for coal based stations. This requirement can be developed post

analysis of minimum operating conditions of coal based power plants. Investments and

retrofitting requirements and costs will have to be factored in.

Balancing Capacity of hydro and availability of gas based stations will have to be improved so that system

operators have the flexibility at hand in order to accommodate the renewable generation.

Storage Storage requirement to reduce variability of renewable generation.

The above cases provides broad level strategies which will have to be undertaken in order to accommodate a

high renewable scenario.


As per the Rajasthan State Renewable Energy Action Plan for Rajasthan, 2017, Rajasthan will require around

1.475 GW balancing reserve to meet net load ramp up/down requirement by FY22 incase renewable target for

the state is met. The plan also provides the ramping requirements as of year FY22.

Table 78: Ramp up/ down requirement of Rajasthan as of FY22

Type Time Quantum Reason

Ramp up 6 to 8 AM ~ 1000 MW To meet morning peak

Ramp down 8 AM to 12 noon ~ 2200 MW

Mainly to accommodate increasing solar

generation. In case base load is lowered

further, higher ramp down than specified will

be required.

Ramp up 4 to 6 PM ~ 1000 MW Due to dip in solar generation

Ramp up 6 to 8 PM ~ 500 MW To meet evening peak

Such ramping requirements will have to be addressed by developing flexible capacities in the state. The state is

expected to have 830 MW as flexible generation capacity, for grid balancing by year 2019, hence additional

645 MW flexible energy forces will have to be developed to balance the grid with RE capacity addition.

The ramping requirements post FY22 up to FY32 will be far higher and additional market instruments,

enhancement of technical capabilities and installation of more storage facilities etc. will have to be brought in to

accommodate higher integration of renewables in the state.

10.5.2. Safe limit of RE integration without storage

Ministry of Power and USAID have undertaken a detailed study at National and Regional level (Greening the

Grid: Pathways to Integrate 175 Gigawatts of Renewable Energy into India’s Electric Grid, Volume I: National

Study, Volume II: Regional study, June 2017) to evaluate pathways for RE integration by FY22. The study was

focused at operational level and was undertaken considering various RE options but mostly the results pertain to

the target of 175 GW installed capacity.

As per the study, by FY22, the integration of RE is possible with minimum of 1.4% curtailment. The results also

highlighted that new fast-ramping infrastructure for the RE, such as storage, is not necessary to manage the

added variability of wind and solar. With the expected transmission and generation capacities, system has the

flexibility to manage added variability and uncertainty. The above conclusion is based on an analysis at national

level and not restricted to a state. At a state level, a restricted study might not lead to an efficient result as a

national level analysis will enable efficient operation and will provide the operator with greater options in flexibly

managing the integration.

The study also highlighted that even though ramping requirement will increase by 27% at a national level, the

same can be met using the ramping facilities of all generating stations. The minimum curtailment of 1.4% is based

on the criteria that thermal plants won’t operate below 55% as specified by CERC. In case further lowering is

allowed, the curtailment can be brought down to 0.76%. In case battery storage is used, the curtailment of 1.4%

can be reduced to 1.1%. However, batteries also have efficiency losses and energy lost in efficiency is expected

to be higher than that lost due to curtailment.

The state specific results for Rajasthan are as given below:

Rajasthan can integrate the equivalent of 49% of total generation in the state by 2022 with 5.6% annual

wind and solar curtailment. Currently, as of FY17, ~55% of the total requirement is generated from its

own generating stations. Out of which ~ 16-18% is currently contributed from RE in the state. Higher RE

contribution will lead to change in the generation mix in the state.


Currently, around 5.6% of wind and solar is curtailed annually and most of these curtailments happen in

majorly the same time. The report suggests that to overcome the same, more transmission planning will

be required for better consumption and export of RE in the state.

With respect to the present situation, by FY 22 the net imports to the state will decrease by 22% but

exports will increase by 11%.

The addition of RE in Rajasthan will change the net load, which is the load that is not met by RE and

therefore must be met by conventional generation. Increased daytime solar generation causes a dip in

net load, which requires Rajasthan to either increase net exports, back down its thermal generators, or

curtail RE.

The PLF of thermal power plants will decrease and generator starts will increase substantially

Low hydro availability in the state will limit its usage in supporting the net load in the state

For the state of Rajasthan, the high level of integration is possible by coordinated planning between all

the stakeholders involved in intrastate transmissions. Further, investment in transmission infrastructure

needs to look into so that RE can be evacuated and flexible reserves of coal and hydro can be used.

The present study results highlight that additional storage facilities might not be required for integration

up to FY22. However, post FY22, storage is expected to play a greater role.

10.5.3. Impact of storage in integration

Energy storage is likely to play a greater role in the power sector of the country in the medium and long term

(post FY22). The drivers for grid-level energy storage are rapidly decreasing cost of energy storage, and the

multitude of benefits provided by energy storage to the grid in general and to grids with high penetration of

renewable energy in particular. Some of the benefits can be to optimize generation by reducing intermittency and

variability, support in reliable operation of grid, frequency regulation, asset optimization in the form of supporting

current balancing assets, reduction in peak demand, acting as reserve and helping in increasing consumption at

a consumer level etc.

As of today, battery storage is considered expensive. However, for several storage technologies, it is expected

that costs will fall with economies of scale. The prices of various storage technologies have fallen at different

rates. For lithium ion batteries, prices have fallen by 73% between 2010 and 2017, as per Bloomberg New Energy

Finance (BNEF), driven mostly by demand from electrical vehicles and improving technology, and are likely to

fall by another 72% by 2030. BNEF’s lithium-ion battery price index shows a fall from $1,000 per kWh in 2010 to

$209 per kWh in 2017. International Renewable Energy Agency (IRENA) estimates that the prices of energy

storage technologies are likely to fall between 50-66% ranges from the present prices by the year 2030,

depending upon technology under consideration.

Taking India as an example, BNEF34 in a recent study have highlighted that the benchmark levellised cost of

electricity (LCOE) from various sources have been declining. BNEF had reported that LCOE for solar PV at $41

per MWh (INR 2.75 per kWh), onshore wind at $39 MWh (INR 2.61 per kWh), coal at $68 per MWh (INR 4.56

per kWh) and combined cycle gas at $93 per MWh (INR 6.23 per kWh). In comparison, in case battery storage

is added to wind and solar, the costs for wind-plus-battery and solar-plus-battery systems in India have wide cost

ranges, of $34-208 per MWh (INR 2.28-13.94 per kWh) and $47-308 per MWh (INR 3.15–20.64 per kWh)

respectively, depending on project characteristics. Unless, the price stabilizes and becomes affordable, the

adoption of storage will take some time. Meantime, storage options other than batteries will have to be utilized to

meet the requirements. As per analysis, in case the price falls to $90 per kWh, it is expected to be at par with

current costs of coal-based stations operating at a base load condition with 60% PLF. Storage costs at $ 147 per

kWh is expected to be at par in case of peak load at 25% PLF.

34 Tumbling costs for wind, solar, batteries are squeezing fossil fuels, 28 March, 2018


Of the many storage options, hydro pumped storage has been prevalent in India. But for other storage systems

like Lithium ion battery based, compressed storage, lead acid based etc., large scale implementation to support

grid level balancing will require some time. The investment in other forms of storage technologies will require

cost benefit analysis and careful selection of the technology.

As of 2017, as per IRENA, the following storage capacities have been announced, contracted and are under

construction in India.

Table 79: Announced, Contracted and Under Construction Storage Capacity by Technology Type

Country Electrochemical

(unspecified) Li Ion battery Total (kW)

India 111000 125 110125

In January 201835, AES India and Mitsubishi corporation started construction of India’s first utility scale energy

storage system of 10 MW capacity that will serve the grid operated by Tata Power Delhi Distribution Limited. The

project has been initiated to demonstrate energy storage and to help create a business case for future

deployment.

In India, out of the available options, pumped storage has been used actively. At present ~ 4.785 GW capacity is

pumped storage capacity, of which ~ 2.45 GW36 is under operation. For the short term, plans may be developed

to restore the available capacity of pumped storage for usage in balancing till other storage technologies are

demonstrated and deployed. Even with decreasing costs, the technologies will have to undergo the phases of

demonstration and deployment up to certain levels before it achieves maturity. Such installations will also have

to be supported by policy and regulatory actions. NITI Aayog in November 2017 released a document highlighting

the potential of battery manufacturing in India with possible pathways and possible policy measures and

incentives. Though it was more towards supporting the goal of EVs in India by 2030, the development of domestic

battery industry would also benefit the usage of batteries in grid balancing. CEA in January 2017 has also brought

out a discussion paper highlighting the technologies along with possible operating models and requirements for

implementation.

However, until a full-fledged deployment takes place, the balancing requirement till FY22 will have to be met

using available sources. Some of the probable solutions which may be looked at:

Ensuring gas availability will enable substantial installed capacities of gas based plants to support in grid

balancing

Possible retrofitting options to support flexible operation in new power plants may be checked so that by

the time the plants are commissioned, it can support higher flexibility

Strengthening of market instruments – short term, power exchanges, ancillary market etc.

Capacity building in utilities for undertaking forecasting and planning

35 India’s first grid-scale electricity storage system, January 2018 accessed from The Economic Times 36 Report on Operational Analysis for Optimization of Hydro Resources & facilitating Renewable Integration in India, June 2017


10.5.4. Need for capacity building

The integration of RE will also call for usage of advanced forecasting in order to plan for power procurement and

investment. Forecasting will have an increasingly important role in the grid with the increase in proportion of RE.

Since solar, wind and hydro energy are dependent on highly variable weather conditions, usage of short term

forecasting involving modeling of weather parameters will be crucial going ahead. Hence it is essential that utilities

start capturing data and develop capacities.

Currently, most of the utilities do not have the expertise to forecast and plan. Hence, capacity building will have

to be undertaken. Broadly, following major heads have been identified across which work has to be done in order:

Co-ordination

• Large scale RE integration has to be carried out with co-ordination - intra state and inter state

• States have to develop co-ordination mechanisms and suitable channels to support integration

Data

• Data is the key and utilities have to manage data in proper formats so that same can be used continuously by the utilities

• Systems and procedures have to be developed to enable sharing of data

Process

• Standard Operating Procedure for undertaking forecasting function has to be developed.

• SoP shall include roles and responsibilities of identified staff, formats for data management, flow of information , methodologies & tools to be deployed, procedure for periodic review and updating of forecasts to maintain relevance

Technology

• Technology requirements will have to be developed by utilities and implemented

• Partial / complete grant based funding to be considered by MoP/ State

• Licences and computer systems for advanced analystical software can be made vailable to Utilities

Organization

• Dedicated staff for forecasting purpose with number, roles & responsibility as per SoP

• Annual training curriculum for requisite skill building to be developed for conitnuos upgradation

• Capacity building should begin to have readiness to deploy advanced techniques as such integration can be forecasted only useing advanced methods

Organisation

Capacity Building

Data

Co-ordination

Process

Technology


11. Data challenges

The study involved extensive use of data which were accessed from various sources. However, challenges were

encountered while accessing and using data. Summary of challenges are highlighted in the following section:

1. Historical data from utilities

The utilities don’t have any formal process through which data for the required period could be accessed

for conducting the study. There was no designated official responsible for providing data for such a study

There was resistance in sharing of data for the study. Concerns regarding usage of the data/ purpose was

raised at various levels

Required data (sales, consumers etc.) was not available for a longer duration for all the three utilities in the

state. Hence a common period of ten years had to be considered.

Data is not maintained in common formats. Every utility had its own formats of reporting and data storage.

This requires additional time in checking and validation of data and to develop a common format for use in

forecasting.

2. Challenges at feeder level data

Supply hour data at feeder level was available only for few selected feeders and in limited circles

Historical supply hour data at feeder level is not available. Hence suitable assumptions had to be built in

for undertaking baseline correction.

Feeder level data has interruption counts of supply and doesn’t directly provide gap in supply hours for

consumer categories

Category (industry/ agriculture etc.) and area wise (urban/ rural) segregation of feeders was not present.

Hence details of supply hour gaps in specific categories could not be worked out.

Data not available in any definite format. The formats varied across utilities and across months.

Since installed feeders are also of varied make and models even in the same utility, the outputs provided

were varied. In few feeders, OEM software are required to access data.

3. Challenges in application of forecasting methodology

Challenge in accessing data for independent variables at a granular level e.g. District/ circle.

Data pertaining to disposable income, historical weather parameters at granular level were not available.

No definite source for accessing data for estimating power demand from infrastructure projects or HT

Industries. In most of the Government offices, there is no designated authority responsible for storage and

upkeep of data pertaining to future projects in the state.

Challenges were encountered in receiving responses for surveys and during data collection from HT

industries and bulk consumers.

No conclusive study could be found to establish the effect of energy efficiency. Various studies projected

different efficiency potentials and scenarios.

T&D loss data available at a state level. Segregated data pertaining to technical losses due to theft, faulty

reading etc. are not maintained.

Data for load factor and diversity factor is available only at a yearly average level for the state/ region.


12. Recommendations

The demand forecasting study using the proposed alternate methodology has involved analysis of extensive data

pertaining to various parameters and for various consumer categories. The methodology has been proposed

considering the gaps which were highlighted in traditional methodologies. Development of the forecast including

baseline correction has been based on availability of data which could be accessed. However, challenges still

remain and will have to be addressed in case similar studies are undertaken in future.

Based on the present study, following are the recommendations which may be looked into:

1. Changes at Policy/ regulatory level

Insufficient/ lack of authentic data is the weakest link for any forecasting study. There are no standard

formats available for capturing of data by the Utilities. The issue may be taken up at policy level viz. CEA/

Electricity Regulators may propose data recording by Utilities in standard formats.

Suggested data formats have been appended below.

2. Establishment of designated authority for data management

Establishment of designated authority (e.g. MIS cell) for managing data is required at DISCOM/ STU

level. Such authority shall record and maintain data in standard formats and also should be responsible

for periodic updating of the data. It should be the single point of contact for third parties to access data.

A governance structure should be in place, which will be responsible for according approvals post

validation checks. This will enable better accessibility of required data and also remove data concerns

which are currently being experienced at various levels.

3. Increased granularity of forecasts

Presently, forecasts are majorly carried out at the Utility or State level. The granularity of the forecasts

needs to be increased by conducting demand forecasting at district level or below (like divisional/sub

divisional level) for better understanding of the behavior and trends and for higher accuracy and use of

the results. Study at a more granular level would be possible only with availability of data at such levels.

4. Periodicity of forecasts to be increased to shorter duration

Currently demand forecasting is carried out after a period of 5 to 10 years. The exercise needs to be

undertaken at shorter period e.g. yearly, to take into account changes in the factors affecting demand.

There should also be a periodic review and updating of the forecasted demand.

5. Use of advanced methods

It is recommended that use of advanced statistical methods should be encouraged. In a recent meet of

the Hon’ble Minister for Power with the Regulators and State DISCOMs, it was mentioned that the

DISCOMSs should be mandated to deploy advanced statistical tools for undertaking demand forecasting

and power procurement planning exercises, the funding of which may be availed from the PSDF fund.

6. Shift towards technological/ Data and Analytics platforms

The idea of undertaking demand forecasting is to effectively plan for infrastructure and capex, optimize

costs, understand future requirements and plan for power procurement. In a scenario, wherein the future

demand will involve interplay for many factors, it is important that forecasting should be undertaken in

technology enabled solutions e.g. forecasting software that is more dynamic and responsive to changes.

Such a transition will also help in improved dash boarding of results.


7. Pilot studies for assessing latent demand

It is recommended that pilot studies may be conducted to understand the factors affecting latent demand

across consumer categories.

8. Use of the alternate methodology in other states

The proposed alternate methodology has been implemented only on the selected state. Enabling

capturing of required data in standard templates can prepare replicable model for baseline correction.

9. Use of baseline correction to undertake forecasts for Rajasthan

It is recommended that while conducting supply side planning for tying up generation capacities, a

baseline correction based approach should be followed in order to minimize the gaps between the actual

demand and required tied up capacities.


13. Annexure

13.1. Forecast results – Circle wise

13.1.1. Alwar

Table 80: Alwar - Forecast of electricity consumption using trend method

(MUs) FY 17 FY 18 FY 19 FY 20 FY 21 FY 22 FY 23 FY 24 FY 25 FY 26 FY 27 FY 28 FY 29 FY 30 FY 31 FY 32

Domestic 545 629 728 846 987 1142 1330 1558 1839 2190 2632 3199 3937 4916 6231 7973

Non-Domestic 190 209 229 251 275 293 312 331 352 373 390 407 424 442 459 477

Public Street Light

11 12 13 14 15 15 15 15 14 14 13 13 12 11 9 8


1441 1540 1644 1755 1871 2038 2218 2412 2620 2844 3085 3342 3617 3911 4224 4557

Agriculture (F) 23 19 16 14 12 9 8 6 5 4 3 2 1 1 1 1


193 198 203 208 214 222 231 240 250 259 270 280 291 302 313 325

Industry (L) 1943 2086 2239 2403 2578 2660 2744 2830 2919 3009 3102 3197 3294 3394 3495 3599

PWW (S + M + L)

47 49 52 55 58 60 63 67 70 73 76 80 83 87 91 95

Mixed Load 21 22 23 24 25 25 25 25 25 24 24 24 24 24 24 24

Total electricity consumption

4414 4764 5148 5569 6034 6465 6945 7484 8093 8791 9595 10544 11685 13087 14848 17059


Table 81: Alwar - Forecast of electricity consumption using HW method


Domestic 591 630 669 708 747 786 825 864 904 943 982 1021 1060 1099 1138 1177

Non-Domestic 236 256 276 296 316 336 356 376 396 416 436 456 476 496 516 536

Public Street Light

10 10 11 12 12 13 14 15 15 16 17 17 18 19 20 20


2276 2420 2565 2710 2854 2999 3144 3288 3433 3578 3722 3867 4012 4156 4301 4446

Agriculture (F) 33 35 36 38 39 40 42 43 45 46 47 49 50 51 53 54


194 203 212 220 229 238 247 256 265 274 282 291 300 309 318 327

Industry (L) 1829 1952 2074 2197 2319 2442 2564 2687 2809 2932 3054 3177 3299 3422 3544 3666

PWW (S + M + L)

48 51 55 58 62 65 69 72 75 79 82 86 89 93 96 100

Mixed Load 38 48 55 61 67 72 76 81 85 89 93 96 100 103 107 110


5254 5604 5952 6299 6645 6991 7336 7681 8026 8371 8715 9060 9404 9748 10092 10436


Table 82: Alwar - Forecast of electricity consumption using econometric method


Domestic 506 550 595 641 688 736 785 834 883 932 980 1027 1072 1114 1152 1175

Non-Domestic 217 244 273 305 340 376 416 458 504 553 595 640 687 736 789 862

Public Street Light

9 10 11 12 12 13 13 14 15 15 16 16 16 17 17 17


1691 1820 1954 2089 2229 2369 2515 2660 2808 2959 3115 3271 3429 3590 3753 3919

Agriculture (F) 25 26 28 29 30 32 33 35 36 38 40 41 43 44 46 48


191 195 198 201 203 204 205 204 202 199 194 187 178 166 151 133

Industry (L) 1820 1942 2064 2186 2305 2427 2549 2671 2792 2911 3033 3154 3276 3398 3516 3637

PWW (S + M + L)

49 54 59 65 72 80 89 100 112 126 142 160 181 205 233 265

Mixed Load 37 47 54 60 66 71 75 80 84 87 91 95 98 102 105 108


4545 4888 5237 5588 5945 6308 6680 7055 7436 7820 8205 8591 8980 9373 9762 10163


13.1.2. Bharatpur & Dholpur

Table 83: Bharatpur and Dholpur - Forecast of electricity consumption using trend method


Domestic 353 389 428 468 510 541 571 597 619 636 646 647 638 618 587 537

Non-Domestic 81 87 93 100 107 116 126 136 147 159 172 186 200 215 232 249

Public Street Light

13 13 13 14 14 14 14 13 13 13 12 11 10 10 8 7


399 429 462 497 534 565 597 631 666 702 726 750 774 798 822 845

Agriculture (F) 18 16 15 13 12 9 8 6 5 4 3 2 1 1 1 0


84 87 89 91 93 96 98 101 103 106 108 111 114 116 119 122

Industry (L) 168 175 182 189 197 202 207 213 218 223 229 235 240 246 252 258

PWW (S + M + L)

43 46 50 53 57 59 61 63 65 67 69 72 74 76 78 81

Mixed Load 10 10 10 10 10 10 10 10 10 10 10 10 10 10 9 9


1170 1253 1342 1436 1534 1612 1691 1769 1846 1920 1975 2022 2061 2090 2108 2108


Table 84: Bharatpur and Dholpur - Forecast of electricity consumption using HW method


Domestic 414 444 475 505 535 565 596 626 656 686 717 747 777 808 838 868

Non-Domestic 118 127 135 144 152 160 168 176 184 192 200 207 215 222 230 237

Public Street Light

17 20 22 24 26 28 30 31 33 34 36 37 38 40 41 42


554 593 632 671 711 750 789 829 868 907 947 986 1025 1064 1104 1143

Agriculture (F) 25 23 21 19 17 14 12 10 8 6 0 0 0 0 0 0


92 102 112 122 134 146 159 172 186 201 217 232 249 266 284 302

Industry (L) 172 192 212 232 253 273 293 313 333 353 373 393 414 434 454 474

PWW (S + M + L)

48 57 63 68 73 78 82 86 90 93 97 100 103 107 110 113

Mixed Load 10 11 12 12 13 14 14 15 16 16 17 17 18 19 19 20


1450 1568 1684 1799 1914 2028 2143 2258 2374 2490 2602 2721 2840 2959 3079 3199


Table 85: Bharatpur and Karauli - Forecast of electricity consumption using econometric method


Domestic 337 367 397 428 460 492 525 558 591 624 656 688 719 747 773 791

Non-Domestic 81 89 97 105 115 124 134 145 156 167 179 192 205 219 233 248

Public Street Light

12 13 14 15 16 17 18 19 21 22 23 24 25 26 27 28


396 430 465 498 530 561 590 615 636 652 663 666 660 644 614 570

Agriculture (F) 18 17 15 14 13 11 10 8 7 5 0 0 0 0 0 0


87 88 90 90 91 91 91 90 89 87 85 83 80 76 71 66

Industry (L) 163 169 176 182 189 196 203 211 218 226 234 242 251 259 268 277

PWW (S + M + L)

43 44 46 48 49 50 50 50 50 49 47 45 41 37 31 24

Mixed Load 10 11 12 12 13 13 14 15 15 16 17 17 18 18 19 20


1147 1229 1311 1393 1475 1556 1635 1711 1782 1848 1905 1957 1998 2026 2037 2024


13.1.3. Dausa and Karauli

Table 86: Dausa and Karuali - Forecast of electricity consumption using trend method


Domestic 268 301 337 375 415 445 473 500 524 543 556 563 560 548 529 488

Non-Domestic 73 80 88 96 105 117 130 145 161 178 198 219 242 268 296 326

Public Street Light

8 8 8 8 8 8 8 8 7 7 7 6 6 5 4 4


695 743 793 846 902 945 988 1033 1078 1125 1172 1221 1270 1319 1369 1420

Agriculture (F) 35 29 24 20 17 13 10 8 6 5 3 2 2 1 1 1


58 62 66 71 76 81 87 93 99 106 113 120 128 137 146 156

Industry (L) 57 63 69 75 82 87 93 99 105 112 119 126 134 142 151 160

PWW (S + M + L)

35 36 38 40 42 43 45 46 48 49 51 52 53 55 56 58

Mixed Load 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5


1235 1328 1429 1537 1653 1745 1839 1936 2033 2130 2224 2315 2401 2480 2558 2618


Table 87: Dausa and Karauli - Forecast of electricity consumption using HW method


Domestic 282 305 328 351 374 397 419 442 465 488 511 534 557 579 602 625

Non-Domestic 132 167 193 216 237 256 275 292 309 325 341 356 371 386 400 414

Public Street Light

12 13 15 17 19 21 23 25 28 30 33 36 38 41 44 47


1003 1071 1139 1207 1275 1343 1410 1478 1546 1614 1682 1750 1818 1886 1954 2022

Agriculture (F) 36 21 6 0 0 0 0 0 0 0 0 0 0 0 0 0


60 63 66 69 72 75 78 81 84 87 90 93 96 100 103 106

Industry (L) 54 55 56 57 58 59 60 62 63 64 65 66 67 68 69 70

PWW (S + M + L)

41 42 43 45 46 47 48 49 50 51 52 54 55 56 57 58

Mixed Load 8 10 12 14 16 18 20 23 25 28 31 34 37 41 46 50


1628 1747 1859 1975 2096 2216 2334 2453 2571 2688 2805 2923 3040 3157 3275 3393


Table 88: Dausa and Karauli - Forecast of electricity consumption using econometric method


Domestic 243 265 289 315 341 369 398 428 460 492 525 558 592 626 658 689

Non-Domestic 67 72 77 82 88 94 100 107 113 120 128 135 143 151 160 168

Public Street Light

8 9 11 13 15 17 19 22 25 29 32 36 41 45 50 56


802 883 969 1057 1149 1242 1339 1435 1531 1627 1724 1815 1902 1983 2055 2116

Agriculture (F) 25 15 5 0 0 0 0 0 0 0 0 0 0 0 0 0


62 68 74 81 88 95 104 113 123 133 145 157 171 185 201 218

Industry (L) 54 55 56 57 58 59 60 61 62 63 64 66 67 68 69 70

PWW (S + M + L)

41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56

Mixed Load 8 10 12 14 16 18 20 22 25 27 30 33 37 40 45 49


1309 1420 1536 1663 1800 1941 2087 2236 2388 2542 2699 2853 3005 3152 3292 3421


13.1.4. Jaipur city circle

Table 89: JCC - Forecast of electricity consumption using trend method


Domestic 1802 1907 2010 2111 2185 2246 2294 2326 2338 2328 2299 2240 2150 2025 1868 1681

Non-Domestic 1123 1200 1281 1367 1458 1542 1630 1722 1817 1916 2018 2124 2233 2346 2461 2579

Public Street Light

87 88 89 90 91 87 82 78 73 67 62 56 50 44 37 31


28 29 30 31 31 32 33 34 34 35 36 36 37 37 38 38

Agriculture (F) 2 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0


276 278 281 284 286 295 303 312 321 330 340 349 359 369 379 390

Industry (L) 577 602 629 657 686 697 707 718 729 740 751 762 772 783 794 805

PWW (S + M + L)

124 125 125 126 126 128 129 131 132 133 134 135 136 137 138 138

Mixed Load 85 87 90 92 94 97 99 102 104 106 109 111 113 115 117 119


4103 4317 4536 4758 4959 5123 5278 5421 5548 5656 5748 5814 5850 5857 5833 5781


Table 90: JCC - Forecast of electricity consumption using HW method


Domestic 1934 2040 2145 2251 2357 2463 2568 2674 2780 2886 2991 3097 3203 3309 3414 3520

Non-Domestic 1289 1342 1396 1450 1504 1557 1611 1665 1718 1772 1826 1880 1933 1987 2041 2095

Public Street Light

101 106 110 115 119 124 128 132 137 141 146 150 155 159 164 168


60 61 62 63 64 65 66 67 67 68 69 70 70 71 72 72

Agriculture (F) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


350 351 355 360 369 380 394 410 427 447 468 490 513 538 563 590

Industry (L) 657 721 791 869 955 1048 1148 1255 1368 1487 1612 1742 1878 2019 2166 2317

PWW (S + M + L)

126 129 132 134 137 140 143 145 148 151 154 157 159 162 165 168

Mixed Load 91 101 112 122 132 142 152 163 173 183 193 204 214 224 234 245


4608 4851 5102 5364 5636 5918 6210 6510 6819 7135 7459 7789 8126 8469 8819 9174


Table 91: JCC - Forecast of electricity consumption using econometric method


Domestic 1793 1917 2045 2174 2284 2392 2499 2605 2707 2808 2901 2991 3074 3147 3207 3256

Non-Domestic 1096 1187 1282 1379 1481 1586 1695 1807 1922 2041 2166 2293 2425 2560 2700 2844

Public Street Light

100 104 109 113 117 120 124 128 131 134 137 139 141 142 143 142


44 46 47 49 50 51 53 54 55 56 58 59 60 61 62 64

Agriculture (F) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


349 350 353 358 366 378 391 407 425 444 464 486 510 534 559 585

Industry (L) 654 717 787 865 949 1041 1141 1247 1359 1476 1600 1730 1865 2005 2148 2298

PWW (S + M + L)

127 132 138 145 154 164 176 191 207 227 249 275 305 339 378 423

Mixed Load 90 100 110 120 130 140 150 160 170 180 190 200 210 220 230 239


4253 4553 4871 5203 5531 5873 6230 6598 6978 7366 7766 8174 8589 9008 9426 9851


13.1.5. JPDC

Table 92: JPDC - Forecast of electricity consumption using trend method


Domestic 471 518 567 618 671 724 776 826 871 910 940 957 951 927 885 825

Non-Domestic 224 244 266 290 316 344 374 406 441 479 519 562 607 656 708 764

Public Street Light

9 9 10 10 10 10 10 10 10 9 9 9 8 8 7 6


1063 1143 1229 1320 1417 1500 1587 1678 1772 1870 1971 2076 2184 2295 2410 2527

Agriculture (F) 354 314 277 245 216 173 139 111 89 71 49 34 24 17 12 8


142 149 157 164 172 176 181 185 189 194 198 203 208 212 217 222

Industry (L) 620 658 698 740 784 802 821 840 859 878 898 918 938 958 978 999

PWW (S + M + L)

57 60 64 68 72 77 81 86 91 97 102 108 114 120 126 133

Mixed Load 9 10 11 11 12 13 14 14 15 16 17 18 19 20 22 23


2950 3105 3278 3467 3671 3819 3982 4156 4337 4523 4703 4884 5053 5214 5366 5506


Table 93: JPDC - Forecast of electricity consumption using HW method


Domestic 461 511 561 610 660 710 760 809 859 909 958 1008 1058 1108 1157 1207

Non-Domestic 275 295 314 334 353 373 392 412 431 450 470 489 509 528 548 567

Public Street Light

8 9 9 10 10 11 11 12 13 13 14 14 15 15 16 16


1781 1975 2169 2363 2557 2751 2945 3139 3333 3527 3721 3915 4109 4303 4497 4691

Agriculture (F) 252 39 0 0 0 0 0 0 0 0 0 0 0 0 0 0


165 178 194 211 229 249 269 290 312 335 359 383 408 434 460 487

Industry (L) 614 664 714 764 814 864 914 964 1014 1064 1114 1164 1214 1264 1314 1364

PWW (S + M + L)

58 63 67 72 76 81 85 89 94 98 103 107 112 116 121 125

Mixed Load 11 13 15 17 19 21 23 25 28 30 32 34 36 38 40 42


3626 3747 4044 4381 4720 5059 5400 5741 6083 6426 6770 7115 7460 7806 8153 8500


Table 94: JPDC - Forecast of electricity consumption using econometric method


Domestic 466 519 578 642 712 789 872 963 1062 1170 1285 1409 1527 1650 1778 1910

Non-Domestic 243 274 310 349 393 440 493 551 615 685 762 846 937 1037 1147 1266

Public Street Light

8 9 10 11 12 13 14 15 16 17 19 20 21 23 24 26


1009 1115 1224 1333 1444 1552 1660 1761 1855 1940 2014 2070 2105 2114 2091 2029

Agriculture (F) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


150 159 168 178 187 196 206 216 227 237 247 258 269 281 292 303

Industry (L) 611 661 711 761 809 859 909 958 1008 1057 1106 1156 1206 1255 1304 1353

PWW (S + M + L)

58 62 66 71 75 79 83 88 92 96 100 104 108 112 116 120

Mixed Load 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41


2556 2813 3083 3361 3651 3951 4261 4579 4903 5231 5565 5896 6209 6509 6790 7048


13.1.6. Jhalawar and Baran

Table 95: Jhalawar and Baran - Forecast of electricity consumption using trend method


Domestic 272 296 320 345 371 409 448 487 526 561 593 617 629 627 612 583

Non-Domestic 73 77 82 87 92 96 100 105 109 114 119 123 128 133 138 143

Public Street Light

15 16 16 17 18 17 17 17 16 16 15 14 13 12 10 9


856 917 981 1048 1120 1174 1230 1288 1347 1408 1470 1533 1597 1662 1728 1795

Agriculture (F) 3 2 1 1 1 1 0 0 0 0 0 0 0 0 0 0


58 60 63 66 68 69 70 71 72 73 74 75 76 77 78 79

Industry (L) 64 68 72 77 82 86 89 93 97 102 106 110 115 120 125 130

PWW (S + M + L)

29 30 32 34 36 38 40 42 45 47 50 52 55 58 60 63

Mixed Load 11 11 12 13 14 16 17 19 21 23 25 27 30 33 36 39


1380 1477 1580 1688 1801 1906 2013 2123 2234 2343 2451 2553 2644 2722 2789 2842


Table 96: Jhalawqr and Baran - Forecast of electricity consumption using HW method


Domestic 296 320 344 367 391 415 438 462 486 510 533 557 581 605 628 652

Non-Domestic 72 78 84 90 96 102 108 114 120 126 132 138 144 150 156 162

Public Street Light

20 25 30 35 40 45 49 54 59 64 69 74 79 83 88 93


1192 1279 1367 1455 1543 1631 1718 1806 1894 1982 2070 2157 2245 2333 2421 2509

Agriculture (F) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


89 89 90 91 93 96 100 104 110 116 123 130 137 145 154 163

Industry (L) 75 90 105 120 135 150 165 180 195 210 226 241 256 271 286 301

PWW (S + M + L)

26 28 30 32 34 36 38 39 41 43 45 47 49 51 52 54

Mixed Load 11 12 13 14 15 16 17 18 19 20 22 23 24 25 26 27


1781 1921 2062 2204 2346 2490 2634 2779 2925 3071 3218 3366 3514 3662 3811 3960


Table 97: Jhalawar and Baran - Forecast of electricity consumption using econometric method


Domestic 245 262 279 296 311 330 348 366 383 398 408 417 419 418 415 409

Non-Domestic 54 60 65 71 76 82 88 94 100 106 113 119 126 132 139 146

Public Street Light

20 25 30 34 39 43 48 52 57 61 65 68 72 75 77 79


757 848 944 1043 1148 1255 1367 1480 1595 1711 1829 1944 2055 2162 2261 2350

Agriculture (F) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


88 89 89 91 93 95 99 104 109 115 122 129 136 144 153 162

Industry (L) 75 90 105 120 134 149 164 179 194 209 224 239 254 269 283 298

PWW (S + M + L)

26 28 30 32 33 35 37 39 40 42 44 45 47 49 50 52

Mixed Load 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26


1277 1413 1554 1699 1849 2007 2169 2333 2498 2663 2825 2984 3132 3273 3403 3521


13.1.7. Kota and Bundi

Table 98: Kota and Bundi - Forecast of electricity consumption using trend method


Domestic 699 756 814 874 933 987 1038 1083 1120 1148 1153 1141 1111 1063 996 910

Non-Domestic 239 255 273 291 310 339 372 406 444 485 528 576 627 681 740 803

Public Street Light

33 34 36 37 38 40 40 41 42 42 41 40 39 37 34 30


723 778 837 900 967 1063 1167 1281 1405 1539 1685 1843 2013 2197 2395 2608

Agriculture (F) 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0


223 232 241 250 259 269 279 290 300 312 323 335 347 360 372 386

Industry (L) 414 429 445 462 480 497 516 535 555 575 596 617 640 662 686 710

PWW (S + M + L)

72 76 81 85 90 92 95 97 99 102 104 106 108 111 113 115

Mixed Load 29 29 29 29 29 29 29 29 30 30 29 29 28 28 27 26


2432 2590 2756 2928 3106 3317 3536 3763 3995 4231 4460 4687 4913 5138 5363 5588


Table 99: Kota and Bundi - Forecast of electricity consumption using HW method


Domestic 749 799 849 899 949 999 1049 1099 1149 1199 1248 1298 1348 1398 1448 1498

Non-Domestic 317 340 362 385 407 430 453 475 498 521 543 566 588 611 634 656

Public Street Light

36 38 41 44 47 49 52 55 58 60 63 66 69 71 74 77


970 1050 1131 1211 1292 1372 1453 1534 1614 1695 1775 1856 1937 2017 2098 2178

Agriculture (F) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


227 239 251 263 275 287 298 310 322 334 346 358 370 381 393 405

Industry (L) 397 407 417 427 437 448 458 468 478 488 499 509 519 529 539 550

PWW (S + M + L)

74 81 87 93 99 105 111 118 124 130 136 142 148 155 161 167

Mixed Load 26 24 21 19 16 13 11 8 6 5 4 5 6 8 11 13


2796 2978 3159 3341 3522 3704 3885 4066 4249 4432 4615 4799 4985 5171 5358 5544


Table 100: Kota and Bundi - Forecast of electricity consumption using econometric method


Domestic 745 802 863 926 991 1055 1121 1190 1259 1331 1393 1450 1506 1559 1607 1658

Non-Domestic 234 262 293 325 359 390 423 458 494 532 573 615 659 706 755 814

Public Street Light

37 41 46 51 57 64 71 79 87 97 107 118 130 142 155 168


702 770 843 920 1005 1073 1146 1223 1303 1388 1478 1573 1672 1777 1887 2038

Agriculture (F) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


234 245 256 268 279 291 303 315 327 340 353 366 379 393 406 421

Industry (L) 395 405 415 425 435 445 455 465 475 485 495 505 515 525 535 545

PWW (S + M + L)

74 80 86 92 98 104 109 115 121 127 133 138 144 149 155 160

Mixed Load 26 24 21 18 16 13 10 8 6 5 4 5 6 8 10 13


2447 2629 2822 3025 3239 3434 3639 3851 4073 4303 4535 4769 5011 5259 5510 5817


13.1.8. Sawai Madhopur and Tonk

Table 101: Sawai Madhopur and Tonk - Forecast of electricity consumption using trend method


Domestic 350 377 404 432 459 485 509 530 548 561 567 566 556 534 500 456

Non-Domestic 89 96 102 110 117 127 137 148 160 173 186 200 215 232 248 266

Public Street Light

8 9 9 9 10 10 10 10 10 10 10 9 9 8 7 7


394 438 487 540 598 651 707 767 831 900 974 1053 1137 1227 1322 1423

Agriculture (F) 9 7 6 5 5 4 3 2 2 1 1 1 0 0 0 0


34 35 36 37 38 39 40 42 43 44 45 47 48 49 51 52

Industry (L) 167 178 189 201 214 224 233 244 254 265 277 288 301 313 327 340

PWW (S + M + L)

154 170 188 207 229 237 246 254 263 272 281 290 299 308 317 326

Mixed Load 7 8 8 9 9 10 10 10 11 11 11 12 12 12 13 13


1213 1317 1430 1551 1680 1786 1895 2008 2122 2237 2352 2466 2578 2684 2785 2883


Table 102: Sawai Madhopur and Tonk - Forecast of electricity consumption using HW method


Domestic 323 353 383 413 443 473 503 533 563 593 623 654 684 714 744 774

Non-Domestic 112 117 123 129 134 140 145 151 157 162 168 173 179 185 190 196

Public Street Light

9 11 12 13 14 15 16 17 19 20 21 22 23 24 25 27


572 614 656 698 740 782 824 866 908 950 992 1034 1076 1118 1160 1202

Agriculture (F) 11 8 5 1 0 0 0 0 0 0 0 0 0 0 0 0


42 42 43 44 45 45 46 47 47 48 49 50 50 51 52 52

Industry (L) 155 157 160 162 164 166 168 171 173 175 177 179 181 184 186 188

PWW (S + M + L)

139 146 154 161 169 177 184 192 199 207 215 222 230 237 245 253

Mixed Load 11 14 16 18 20 22 24 26 28 30 32 34 37 39 42 45


1373 1462 1551 1638 1728 1820 1911 2002 2094 2185 2277 2368 2460 2552 2644 2736


Table 103: Sawai Madhopur and Tonk - Forecast of electricity consumption using econometric method


Domestic 352 385 420 455 490 527 564 601 638 675 712 760 787 812 833 851

Non-Domestic 83 90 98 106 115 124 134 144 155 166 178 191 204 218 233 249

Public Street Light

9 11 12 14 15 17 19 22 24 27 30 33 37 40 44 48


291 331 374 419 466 516 568 622 678 735 795 856 917 979 1041 1101

Agriculture (F) 7 5 3 0 0 0 0 0 0 0 0 0 0 0 0 0


42 42 43 44 44 45 46 46 47 48 48 49 50 51 51 52

Industry (L) 154 157 159 161 163 165 167 169 172 174 176 178 180 182 184 187

PWW (S + M + L)

148 169 192 218 247 280 316 356 401 450 506 568 637 713 799 894

Mixed Load 11 14 16 18 20 21 23 25 27 29 32 34 36 39 41 44


1098 1205 1317 1434 1561 1695 1836 1985 2141 2305 2477 2669 2849 3035 3227 3426


13.1.9. Ajmer

Table 104: Ajmer - Forecast of electricity consumption using trend method


Domestic 577 621 665 709 753 800 845 885 919 946 971 984 974 944 896 830

Non-Domestic 216 240 267 296 329 345 361 377 394 412 430 448 466 485 504 523

Public Street Light

12 13 13 14 14 15 15 15 15 15 14 14 13 12 11 10


187 210 236 265 297 328 362 400 441 485 534 587 645 707 775 848

Agriculture (F) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


324 339 356 373 392 413 436 459 484 511 538 567 597 629 662 697

Industry (L) 531 557 585 614 645 677 710 745 781 819 871 925 983 1044 1109 1177

PWW (S + M + L)

124 132 140 149 158 160 161 163 164 165 165 165 165 164 162 160

Mixed Load 37 39 41 43 45 45 45 45 45 45 45 45 45 44 44 44


2008 2151 2303 2464 2632 2782 2934 3089 3244 3397 3568 3735 3888 4030 4164 4289


Table 105: Ajmer - Forecast of electricity consumption using HW method


Domestic 652 714 762 803 840 874 906 936 965 992 1018 1043 1067 1091 1114 1136

Non-Domestic 267 282 297 312 327 342 356 371 386 401 416 431 446 461 476 491

Public Street Light

13 13 14 15 16 17 17 18 19 20 21 21 22 23 24 25


260 286 311 336 362 387 412 438 463 489 514 539 565 590 615 641

Agriculture (F) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


327 344 360 377 394 410 427 443 460 477 493 510 526 543 560 576

Industry (L) 551 588 625 662 700 737 774 812 849 886 924 961 998 1035 1073 1110

PWW (S + M + L)

127 136 144 153 162 171 180 189 197 206 215 224 233 242 250 259

Mixed Load 34 34 33 32 32 31 31 30 30 29 29 28 28 27 27 26


2231 2396 2547 2691 2832 2969 3104 3238 3369 3500 3629 3758 3885 4012 4138 4264


Table 106: Ajmer - Forecast of electricity consumption using econometric method


Domestic 574 617 662 708 754 801 849 896 944 992 1038 1084 1120 1149 1173 1193

Non-Domestic 187 206 226 248 271 287 303 320 338 356 376 395 416 437 458 492

Public Street Light

12 13 14 15 15 16 17 18 18 19 19 20 20 21 21 21


199 225 253 283 316 352 391 433 478 526 579 635 695 760 829 903

Agriculture (F) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


322 338 354 371 387 404 422 439 458 476 495 514 534 554 573 594

Industry (L) 540 571 605 640 676 715 755 798 843 889 938 990 1045 1102 1161 1225

PWW (S + M + L)

129 141 154 169 185 203 224 247 272 300 332 367 406 450 498 552

Mixed Load 34 33 33 32 32 31 31 30 29 29 28 28 27 27 26 26


1995 2144 2301 2465 2636 2809 2991 3181 3380 3587 3805 4033 4263 4498 4741 5005


13.1.10. Bhilwara

Table 107: Bhilwara - Forecast of electricity consumption using trend method


Domestic 364 391 419 447 474 502 528 551 570 584 597 603 594 574 543 501

Non-Domestic 120 136 154 174 197 206 216 226 237 248 259 270 282 293 305 317

Public Street Light

16 18 20 22 25 25 26 26 27 27 26 26 25 23 22 19


338 373 410 452 497 536 578 623 670 721 779 841 907 978 1052 1132

Agriculture (F) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


153 163 175 187 200 210 222 233 246 259 272 287 301 317 333 351

Industry (L) 618 641 665 690 715 741 768 796 825 855 908 965 1025 1089 1157 1228

PWW (S + M + L)

29 29 30 31 31 31 32 32 32 32 32 32 31 31 30 30

Mixed Load 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4


1642 1756 1877 2006 2143 2257 2374 2492 2611 2729 2878 3027 3170 3310 3447 3581


Table 108: Bhilwara - Forecast of electricity consumption using HW method


Domestic 403 426 449 472 496 519 542 565 588 612 635 658 681 704 728 751

Non-Domestic 138 141 145 151 157 163 170 177 185 192 200 208 215 223 231 239

Public Street Light

13 14 15 16 18 19 20 21 22 24 25 26 27 28 30 31


560 607 653 700 746 792 839 885 932 978 1025 1071 1117 1164 1210 1257

Agriculture (F) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


150 157 165 172 179 187 194 201 208 216 223 230 238 245 252 260

Industry (L) 627 630 633 636 639 642 645 648 651 655 658 661 664 667 670 673

PWW (S + M + L)

35 36 38 40 41 43 45 47 48 50 52 53 55 57 59 60

Mixed Load 6 8 9 9 10 11 12 12 13 14 15 15 16 17 17 18


1932 2019 2107 2197 2286 2377 2467 2558 2649 2740 2831 2922 3014 3105 3197 3289


Table 109: Bhilwara - Forecast of electricity consumption using econometric method


Domestic 364 395 426 458 490 524 557 591 625 659 691 724 750 771 789 804

Non-Domestic 97 107 117 128 140 148 157 166 175 185 195 205 216 227 238 255

Public Street Light

16 18 21 24 27 31 35 40 46 51 58 65 73 81 90 99


326 358 393 431 473 518 567 619 677 739 807 881 962 1049 1145 1250

Agriculture (F) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


150 157 165 171 178 183 189 193 197 200 202 204 205 204 203 200

Industry (L) 624 627 630 633 635 638 641 644 648 650 653 656 659 662 664 668

PWW (S + M + L)

34 36 38 39 41 42 44 46 47 49 50 52 53 55 56 58

Mixed Load 6 8 9 9 10 11 12 12 13 14 14 15 16 16 17 18


1617 1706 1799 1895 1994 2096 2202 2312 2427 2546 2672 2802 2933 3066 3203 3352


13.1.11. Nagaur

Table 110: Nagaur - Forecast of electricity consumption using trend method


Domestic 329 358 388 418 449 480 510 538 562 582 601 613 611 596 570 531

Non-Domestic 101 118 137 159 184 199 214 230 247 265 285 305 327 350 374 399

Public Street Light

12 13 15 17 19 20 22 23 23 24 25 25 25 24 23 21


966 1033 1104 1178 1257 1340 1427 1519 1615 1715 1821 1930 2045 2164 2287 2414

Agriculture (F) 236 215 195 177 160 145 131 119 107 97 87 79 71 64 57 51


128 134 140 146 153 158 165 171 177 184 191 198 206 213 221 229

Industry (L) 125 131 136 143 149 157 165 174 183 193 205 218 232 246 261 277

PWW (S + M + L)

77 82 87 92 97 99 100 101 102 103 103 103 103 103 102 101

Mixed Load 6 6 6 6 6 6 6 6 7 7 7 7 7 7 7 7


1981 2089 2207 2336 2474 2604 2740 2880 3024 3170 3325 3479 3625 3766 3901 4030


Table 111: Nagaur - Forecast of electricity consumption using HW method


Domestic 396 463 517 565 611 654 696 736 775 814 851 889 925 961 997 1032

Non-Domestic 109 117 124 132 139 147 154 162 169 176 184 191 199 206 214 221

Public Street Light

16 18 20 21 23 24 25 26 27 29 30 31 32 32 33 34


1406 1528 1650 1772 1894 2016 2138 2260 2381 2503 2625 2747 2869 2991 3113 3235

Agriculture (F) 251 196 149 111 78 66 74 0 0 0 0 0 0 0 0 0


130 136 142 148 155 161 167 173 179 185 191 197 203 209 215 222

Industry (L) 136 153 169 185 202 218 234 251 267 283 299 316 332 348 365 381

PWW (S + M + L)

93 103 111 118 124 130 136 141 146 151 156 160 165 169 173 177

Mixed Load 6 7 8 9 10 10 11 12 13 14 15 15 16 17 18 19


2544 2721 2891 3062 3235 3426 3634 3760 3958 4155 4351 4546 4741 4935 5128 5321


Table 112: Nagaur - Forecast of electricity consumption using econometric method


Domestic 426 467 510 557 606 654 704 758 813 871 930 992 1048 1099 1148 1204

Non-Domestic 102 116 132 148 167 183 200 218 237 257 279 302 326 351 378 413

Public Street Light

11 12 13 14 15 17 18 19 20 22 23 24 26 27 28 29


951 1014 1077 1138 1199 1257 1313 1362 1406 1442 1470 1485 1486 1470 1434 1373

Agriculture (F) 180 142 110 83 59 50 57 0 0 0 0 0 0 0 0 0


133 140 147 154 162 169 177 184 192 200 208 217 225 234 243 252

Industry (L) 136 153 173 195 220 249 282 320 362 411 467 530 602 685 778 884

PWW (S + M + L)

92 102 110 116 122 128 133 138 143 147 152 156 160 163 167 170

Mixed Load 6 7 8 9 9 10 11 12 13 14 14 15 16 17 18 18


2038 2153 2279 2415 2560 2717 2895 3011 3187 3364 3543 3720 3888 4046 4193 4344


13.1.12. Udaipur

Table 113: Udaipur - Forecast of electricity consumption using trend method


Domestic 513 555 597 641 684 727 767 803 835 858 881 893 884 857 813 753

Non-Domestic 251 278 309 342 379 406 434 464 496 529 565 601 640 681 723 767

Public Street Light

14 14 15 16 16 16 16 16 15 15 14 13 12 11 10 9


205 229 255 283 315 339 365 393 422 453 487 522 558 597 638 681

Agriculture (F) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


104 106 107 109 111 113 116 118 121 123 126 129 132 134 137 140

Industry (L) 382 399 417 435 454 479 504 531 560 589 627 666 707 751 798 847

PWW (S + M + L)

47 49 51 53 55 57 58 60 61 62 63 64 65 66 66 66

Mixed Load 25 26 26 27 27 28 28 29 29 30 30 31 31 31 32 32


1541 1655 1777 1906 2041 2164 2289 2414 2539 2661 2793 2918 3029 3128 3217 3294


Table 114: Udaipur - Forecast of electricity consumption using HW method


Domestic 591 639 686 734 782 830 878 926 974 1022 1070 1117 1165 1213 1261 1309

Non-Domestic 304 322 339 356 373 390 408 425 442 459 477 494 511 528 546 563

Public Street Light

18 19 20 21 22 23 25 26 27 28 30 31 32 33 35 36


300 316 332 349 365 382 398 414 431 447 524 540 556 573 589 606

Agriculture (F) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


106 108 110 112 114 116 119 121 123 125 127 129 132 134 136 138

Industry (L) 386 443 501 558 616 673 731 789 846 904 997 1055 1112 1170 1228 1285

PWW (S + M + L)

48 51 54 57 61 64 67 70 73 77 80 83 86 90 93 96

Mixed Load 26 29 33 36 40 43 47 50 54 57 60 64 67 71 74 78


1777 1926 2075 2224 2373 2522 2671 2820 2970 3119 3364 3513 3663 3812 3961 4110


Table 115: Udaipur - Forecast of electricity consumption using econometric method


Domestic 492 532 573 615 658 701 745 789 833 877 920 962 996 1023 1046 1066

Non-Domestic 239 264 291 319 350 377 405 435 466 499 533 569 606 646 687 737

Public Street Light

14 16 17 18 20 21 22 22 23 24 25 26 27 27 28 30


204 223 233 252 281 318 329 342 352 371 392 415 438 463 489 517

Agriculture (F) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


106 108 111 113 116 119 122 125 129 132 136 140 144 148 152 157

Industry (L) 384 441 498 556 612 669 727 784 841 897 990 1047 1105 1162 1218 1275

PWW (S + M + L)

47 50 53 57 60 63 66 69 72 75 78 81 84 86 89 92

Mixed Load 25 29 32 36 39 42 46 49 53 56 60 63 66 70 73 76


1511 1663 1809 1966 2136 2310 2462 2616 2769 2931 3134 3302 3465 3625 3782 3949


13.1.13. Rajsamand

Table 116: Rajsamand - Forecast of electricity consumption using trend method


Domestic 162 173 185 197 209 221 233 243 251 257 263 265 261 252 238 220

Non-Domestic 49 53 57 61 66 71 76 81 86 92 98 104 111 118 125 132

Public Street Light

6 7 8 9 10 11 12 13 14 15 15 15 14 14 13 12


81 86 92 98 104 111 118 125 133 141 152 164 176 190 204 218

Agriculture (F) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


256 276 296 318 342 359 376 395 414 434 455 476 499 523 548 574

Industry (L) 267 279 290 302 315 324 333 343 353 363 386 410 436 463 491 522

PWW (S + M + L)

14 15 16 16 17 18 18 18 18 19 19 19 19 19 19 19

Mixed Load 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3


837 890 946 1004 1065 1116 1167 1220 1271 1323 1390 1456 1520 1581 1641 1699


Table 117: Rajsamand - Forecast of electricity consumption using HW method


Domestic 178 186 194 202 211 219 227 235 244 252 260 268 276 285 293 301

Non-Domestic 62 65 69 72 76 80 83 87 90 94 97 101 105 108 112 115

Public Street Light

6 8 9 10 11 12 13 14 16 17 18 19 20 21 23 24


117 133 148 164 179 195 210 225 241 256 272 287 302 318 333 349

Agriculture (F) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


260 279 298 317 336 355 374 393 412 432 451 470 489 508 527 546

Industry (L) 258 258 258 258 258 258 258 258 258 258 258 258 258 258 258 258

PWW (S + M + L)

13 14 14 15 16 16 17 18 18 19 20 20 21 22 22 23

Mixed Load 3 3 4 4 4 5 5 6 6 7 7 8 8 9 9 10


897 945 994 1043 1091 1140 1188 1237 1286 1334 1383 1431 1480 1529 1577 1626


Table 118: Rajsamand - Forecast of electricity consumption using econometric method


Domestic 158 171 184 198 211 226 240 255 270 286 301 316 329 339 349 357

Non-Domestic 47 52 58 64 71 77 84 91 98 106 114 122 132 141 151 164

Public Street Light

7 7 8 9 10 11 12 13 15 16 17 17 18 18 19 20


78 84 91 98 106 117 129 142 155 170 186 203 222 241 263 282

Agriculture (F) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


261 284 308 332 357 383 411 439 468 498 529 561 594 629 664 701

Industry (L) 257 257 257 257 257 257 257 257 257 256 256 256 256 256 256 256

PWW (S + M + L)

13 14 14 15 15 16 17 17 18 18 19 20 20 21 21 22

Mixed Load 3 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10


822 872 923 977 1032 1092 1155 1219 1287 1356 1429 1504 1579 1655 1733 1812


13.1.14. Chittorgarh and Pratapgarh

Table 119: Chitorgarh and Pratapgarh - Forecast of electricity consumption using trend method


Domestic 293 316 339 362 385 410 434 456 475 489 504 511 508 493 469 435

Non-Domestic 73 80 88 97 107 114 121 129 137 146 155 164 174 184 194 205

Public Street Light

9 10 11 12 13 13 14 15 15 15 16 16 15 15 14 13


853 918 988 1063 1142 1226 1315 1409 1509 1615 1726 1843 1966 2095 2231 2372

Agriculture (F) 16 10 7 4 3 2 1 1 0 0 0 0 0 0 0 0


45 46 47 48 49 51 53 55 57 58 60 62 64 67 69 71

Industry (L) 205 214 223 233 243 256 270 285 300 316 336 357 379 402 427 454

PWW (S + M + L)

34 35 36 37 38 38 38 38 38 38 38 38 37 37 36 36

Mixed Load 21 21 22 22 23 23 23 23 23 23 23 23 23 23 22 22


1549 1651 1761 1879 2003 2134 2270 2410 2554 2701 2857 3014 3166 3315 3462 3607


Table 120: Chitorgarh and Pratapgarh - Forecast of electricity consumption using HW method


Domestic 345 384 417 447 476 504 531 558 584 609 635 660 684 709 733 757

Non-Domestic 87 94 101 109 116 123 131 138 145 153 160 167 175 182 189 197

Public Street Light

11 12 13 14 15 16 16 17 18 19 20 20 21 22 23 24


1134 1231 1329 1427 1525 1622 1720 1818 1916 2013 2111 2209 2307 2404 2502 2600

Agriculture (F) 18 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0


46 46 46 46 46 46 46 46 46 45 45 45 45 45 45 45

Industry (L) 408 434 450 460 466 471 473 474 474 473 471 469 466 464 461 457

PWW (S + M + L)

35 36 38 39 41 42 44 45 47 48 50 51 53 54 56 57

Mixed Load 21 22 23 25 26 27 28 30 31 32 34 35 36 37 39 40


2105 2260 2417 2566 2710 2851 2989 3125 3259 3392 3525 3656 3787 3917 4047 4177


Table 121: Chitorgarh and Pratapgarh - Forecast of electricity consumption using econometric method


Domestic 290 317 344 372 401 431 461 492 523 555 586 617 643 666 685 703

Non-Domestic 62 69 76 83 91 99 108 117 126 136 146 157 169 181 194 208

Public Street Light

11 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


871 966 1065 1168 1275 1385 1501 1620 1742 1870 2004 2140 2281 2427 2578 2734

Agriculture (F) 13 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0


44 46 48 51 53 55 57 59 61 63 65 67 68 70 71 71

Industry (L) 406 432 447 458 464 468 470 471 471 469 468 466 463 460 457 454

PWW (S + M + L)

36 39 42 46 50 54 59 65 71 78 86 94 104 114 126 139

Mixed Load 21 22 23 24 26 27 28 29 31 32 33 34 35 37 38 39


1755 1891 2046 2201 2359 2519 2684 2852 3025 3202 3387 3575 3764 3955 4149 4349


13.1.15. Banswara and Dungarour

Table 122: Banswara and Dungarpur - Forecast of electricity consumption using trend method


Domestic 343 376 411 447 485 524 564 602 637 667 698 720 726 717 694 654

Non-Domestic 62 68 75 83 92 97 103 109 115 122 128 135 143 150 158 166

Public Street Light

5 5 5 6 6 7 7 7 7 7 7 7 7 6 6 5


179 196 214 234 256 279 304 331 360 391 420 451 484 519 555 594

Agriculture (F) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


33 34 35 36 36 37 38 39 40 41 41 42 43 43 44 45

Industry (L) 72 75 78 82 85 90 95 100 105 110 117 125 132 141 149 159

PWW (S + M + L)

14 15 15 16 16 17 17 17 18 18 18 19 19 19 19 19

Mixed Load 5 5 6 6 6 5 4 3 2 2 1 1 1 1 0 0


712 774 840 909 982 1055 1131 1207 1283 1358 1432 1500 1554 1596 1626 1642


Table 123: Banswara and Dungarpur - Forecast of electricity consumption using HW method


Domestic 371 395 418 442 466 490 514 538 562 586 609 633 657 681 705 729

Non-Domestic 79 84 89 94 98 103 108 113 118 123 128 133 138 143 148 153

Public Street Light

5 6 6 7 7 8 9 9 10 10 11 12 12 13 13 14


247 265 283 300 318 336 354 372 390 408 425 443 461 479 497 515

Agriculture (F) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


33 34 35 36 37 38 39 40 41 42 43 44 44 45 46 47

Industry (L) 95 115 136 156 177 198 218 239 260 280 301 321 342 363 383 404

PWW (S + M + L)

15 16 16 16 17 17 18 18 19 19 19 20 20 21 21 22

Mixed Load 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21


850 921 991 1061 1131 1202 1272 1342 1412 1483 1553 1623 1693 1764 1834 1904


Table 124: Banswara and Dungarpur - Forecast of electricity consumption using econometric method


Domestic 322 345 369 393 417 440 464 487 510 532 552 572 587 597 605 610

Non-Domestic 63 70 78 86 94 101 109 117 125 133 142 152 162 172 183 197

Public Street Light

5 5 5 6 6 7 7 8 8 8 9 9 9 10 10 11


170 188 207 227 249 272 297 323 351 381 412 446 481 519 559 602

Agriculture (F) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


33 34 35 36 37 38 39 40 40 41 42 43 44 45 46 47

Industry (L) 94 115 135 156 176 197 217 238 258 278 299 319 340 360 380 401

PWW (S + M + L)

15 15 16 16 17 17 17 18 18 19 19 19 20 20 20 21

Mixed Load 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21


708 780 854 929 1006 1083 1162 1242 1324 1407 1492 1578 1661 1742 1823 1908


13.1.16. Jhunjhunu

Table 125: Jhunjhunu - Forecast of electricity consumption using trend method


Domestic 347 376 406 436 467 496 524 550 572 589 605 614 608 590 561 520

Non-Domestic 108 121 135 151 169 188 208 230 255 282 306 332 360 391 423 457

Public Street Light

8 9 10 12 13 14 15 16 17 17 18 18 17 17 16 15


468 512 559 610 665 710 758 808 861 916 924 930 937 942 946 949

Agriculture (F) 391 373 355 338 321 305 290 275 261 247 234 221 209 197 185 174


35 38 41 44 48 51 55 60 64 69 73 78 82 87 92 97

Industry (L) 61 65 68 72 76 81 87 92 99 105 113 122 131 142 153 165

PWW (S + M + L)

85 92 99 107 115 123 130 138 146 154 156 157 158 159 159 159

Mixed Load 8 9 9 10 10 11 12 13 14 15 16 17 18 19 20 21


1513 1594 1684 1781 1885 1980 2079 2182 2287 2393 2444 2488 2521 2542 2554 2556


Table 126: Jhunjhunu - Forecast of electricity consumption using HW method


Domestic 383 460 471 494 529 574 627 686 752 823 898 978 1062 1151 1243 1338

Non-Domestic 127 129 138 147 156 165 174 182 191 200 209 218 227 235 244 253

Public Street Light

8 9 11 13 14 16 17 19 21 22 24 25 27 29 30 32


589 644 669 694 720 745 770 795 820 845 870 895 920 945 970 995

Agriculture (F) 608 550 548 546 544 542 541 539 538 536 535 533 532 531 530 529


32 34 35 37 38 39 41 42 43 45 46 47 49 50 51 53

Industry (L) 62 95 109 117 124 129 133 136 138 141 142 144 145 146 146 147

PWW (S + M + L)

80 84 89 93 98 103 108 112 117 122 127 131 136 141 146 150

Mixed Load 8 11 13 14 15 16 18 19 20 22 23 24 26 27 28 29


1896 2018 2083 2156 2238 2329 2427 2531 2640 2755 2873 2996 3123 3254 3388 3526


Table 127: Jhunjhunu - Forecast of electricity consumption using econometric method


Domestic 343 373 406 440 476 515 555 596 640 685 731 778 821 860 896 932

Non-Domestic 118 137 157 180 205 229 255 284 315 349 375 402 432 463 496 551

Public Street Light

7 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


387 426 468 513 560 572 583 589 593 593 590 581 566 545 518 549

Agriculture (F) 436 400 404 407 411 415 419 422 426 430 434 438 441 445 449 453


38 40 43 46 49 52 55 59 63 67 71 76 81 86 92 98

Industry (L) 61 94 108 117 123 128 132 135 138 140 141 143 144 145 145 145

PWW (S + M + L)

78 83 87 92 96 101 106 111 115 120 125 130 135 140 145 151

Mixed Load 8 11 12 14 15 16 18 19 20 21 23 24 25 26 28 29


1476 1565 1686 1808 1935 2028 2122 2216 2310 2404 2490 2571 2645 2711 2769 2907


13.1.17. Sikar

Table 128: Sikar - Forecast of electricity consumption using trend method


Domestic 432 465 497 531 563 600 636 668 696 718 740 752 747 727 692 643

Non-Domestic 131 145 160 177 196 216 239 263 289 318 343 370 398 429 461 495

Public Street Light

8 8 9 10 10 10 11 11 11 11 11 10 10 9 8 7


839 913 992 1077 1169 1263 1363 1471 1585 1707 1845 1992 2149 2316 2493 2681

Agriculture (F) 273 218 174 139 111 88 70 56 44 35 28 22 18 14 11 9


69 72 76 79 83 87 91 95 100 104 109 114 119 125 130 136

Industry (L) 165 177 190 204 218 234 251 270 289 310 329 350 372 395 419 445

PWW (S + M + L)

81 86 91 96 101 106 112 117 123 128 134 139 144 149 153 157

Mixed Load 5 5 5 5 6 6 6 7 7 7 8 8 8 9 9 10


2002 2088 2194 2318 2457 2612 2779 2957 3144 3338 3546 3757 3965 4171 4377 4582


Table 129: Table 92: Sikar - Forecast of electricity consumption using HW method


Domestic 465 506 543 580 618 655 692 729 766 803 840 878 915 952 989 1026

Non-Domestic 153 160 169 178 187 196 206 215 224 233 242 251 260 269 278 287

Public Street Light

7 10 13 16 19 22 25 28 31 34 38 41 44 47 50 53


1102 1368 1506 1643 1780 1918 2055 2192 2330 2467 2604 2742 2879 3016 3154 3291

Agriculture (F) 456 285 98 0 0 0 0 0 0 0 0 0 0 0 0 0


67 70 74 78 82 86 90 93 97 101 105 109 113 116 120 124

Industry (L) 151 128 171 203 231 256 279 300 321 341 360 378 396 414 432 449

PWW (S + M + L)

77 82 87 93 98 104 109 115 120 126 131 137 142 148 153 158

Mixed Load 5 6 6 6 6 6 7 7 7 7 8 8 8 8 8 9


2482 2616 2667 2798 3022 3243 3462 3679 3896 4112 4327 4542 4757 4970 5184 5397


Table 130: Sikar - Forecast of electricity consumption using econometric method


Domestic 418 453 492 531 573 617 663 711 761 813 865 918 966 1009 1050 1088

Non-Domestic 147 166 188 210 235 261 289 319 352 387 417 447 480 515 552 605

Public Street Light

8 9 10 11 12 13 15 17 19 21 23 25 28 31 33 36


768 818 873 929 990 1054 1123 1195 1271 1352 1439 1530 1627 1729 1837 1952

Agriculture (F) 327 207 72 0 0 0 0 0 0 0 0 0 0 0 0 0


70 75 80 85 91 97 103 110 117 124 132 141 149 159 168 178

Industry (L) 150 128 170 202 230 254 277 298 319 338 357 376 394 411 428 445

PWW (S + M + L)

82 88 96 104 112 121 131 143 154 167 182 197 214 232 253 275

Mixed Load 5 6 6 6 6 6 7 7 7 7 7 8 8 8 8 8


1974 1951 1985 2079 2249 2425 2609 2800 3000 3209 3422 3642 3866 4094 4330 4589


13.1.18. Jodhpur City circle

Table 131: JCC - Forecast of electricity consumption using trend method


Domestic 443 466 488 510 530 555 578 598 612 622 629 629 614 587 550 502

Non-Domestic 250 270 291 313 336 367 400 435 474 515 562 613 668 728 792 860

Public Street Light

90 90 91 91 91 105 122 140 160 181 197 212 224 232 235 230


13 15 17 19 22 24 26 28 30 33 35 38 40 43 46 49

Agriculture (F) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


144 146 148 150 152 158 165 171 178 185 187 189 192 194 197 199

Industry (L) 286 296 306 316 326 340 354 368 383 399 412 425 438 452 466 480

PWW (S + M + L)

36 37 38 38 39 39 39 39 39 39 38 37 36 35 34 32

Mixed Load 96 98 100 101 103 105 106 108 109 111 112 113 114 115 116 117


1359 1418 1478 1538 1599 1693 1789 1887 1986 2083 2172 2256 2327 2386 2434 2470


Table 132: JCC - Forecast of electricity consumption using HW method


Domestic 583 613 643 672 702 732 762 792 821 851 881 911 941 971 1000 1030

Non-Domestic 307 327 346 365 384 403 422 441 460 479 498 517 536 556 575 594

Public Street Light

78 86 93 101 108 116 123 131 138 146 154 161 169 176 184 191


16 16 16 16 17 17 17 18 18 18 18 19 19 19 20 20

Agriculture (F) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


157 164 170 177 184 191 198 205 212 219 225 232 239 246 253 260

Industry (L) 303 303 303 303 303 303 303 303 303 303 303 303 303 303 303 303

PWW (S + M + L)

36 37 38 39 40 40 41 42 43 44 45 45 46 47 48 49

Mixed Load 98 105 111 117 124 130 136 143 149 156 162 168 175 181 187 194


1578 1649 1720 1791 1861 1932 2003 2074 2144 2215 2286 2357 2428 2498 2569 2640


Table 133: JCC - Forecast of electricity consumption using econometric method


Domestic 487 518 551 586 622 660 699 740 782 825 869 914 952 986 1017 1046

Non-Domestic 259 286 316 346 379 417 458 501 546 594 631 669 709 750 793 849

Public Street Light

77 85 92 99 106 113 120 126 132 138 144 149 154 157 160 162


12 14 15 16 18 19 21 23 25 27 29 32 34 38 41 46

Agriculture (F) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


156 163 170 176 183 190 197 204 210 217 224 231 238 244 251 258

Industry (L) 301 301 301 301 301 301 301 301 301 301 301 301 301 301 300 300

PWW (S + M + L)

36 37 37 38 39 40 40 41 42 43 43 44 45 45 46 47

Mixed Load 97 104 110 116 122 128 135 141 147 153 159 165 171 178 184 190


1426 1507 1592 1680 1770 1868 1970 2076 2185 2298 2400 2504 2603 2699 2792 2896


13.1.19. Jodhpur district circle

Table 134: Jodhpur DC - Forecast of electricity consumption using trend method


Domestic 240 269 300 334 370 393 416 437 455 469 482 489 486 472 449 410

Non-Domestic 73 79 86 93 101 110 121 132 145 158 174 191 210 231 253 274

Public Street Light

3 3 4 4 4 5 5 5 5 4 4 4 4 4 3 3


2181 2426 2697 2996 3325 3561 3810 4072 4350 4641 4841 5045 5252 5462 5674 6053

Agriculture (F) 467 408 357 312 273 250 228 209 191 174 159 145 132 120 109 0


94 103 113 124 136 142 149 155 162 170 177 185 193 201 210 213

Industry (L) 69 74 79 84 89 92 95 98 102 105 108 112 115 119 122 126

PWW (S + M + L)

253 266 279 292 306 311 316 320 324 327 329 330 331 330 328 315

Mixed Load 22 23 24 25 26 26 26 25 25 25 25 25 25 24 24 24


3401 3651 3938 4264 4630 4890 5165 5454 5757 6073 6300 6526 6747 6963 7173 7419


Table 135: Jodhpur DC - Forecast of electricity consumption using HW method


Domestic 256 284 312 341 369 397 425 454 482 510 538 566 595 623 651 679

Non-Domestic 90 100 110 120 130 140 150 160 170 180 190 200 210 219 229 239

Public Street Light

3 3 3 3 4 4 4 4 4 5 5 5 5 5 6 6


3101 3376 3650 3925 4200 4475 4749 5024 5299 5573 5848 6123 6398 6672 6947 7222

Agriculture (F) 510 420 373 345 329 320 317 320 325 334 346 360 376 394 413 434


93 100 107 113 120 127 133 140 147 153 160 167 173 180 187 193

Industry (L) 64 64 64 64 64 63 63 63 63 63 62 62 62 62 62 62

PWW (S + M + L)

264 282 301 319 338 357 375 394 412 431 449 468 486 505 523 542

Mixed Load 22 27 31 35 40 44 49 53 58 62 66 71 75 80 84 88


4404 4657 4952 5266 5592 5926 6266 6611 6959 7310 7665 8021 8379 8740 9101 9465


Table 136: Jodhpur DC - Forecast of electricity consumption using econometric method


Domestic 165 185 206 228 252 278 305 334 364 396 430 464 497 528 558 587

Non-Domestic 67 74 81 88 96 105 114 123 134 145 156 169 182 196 211 226

Public Street Light

3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5


2281 2527 2787 3053 3334 3621 3923 4233 4554 4886 5235 5591 5960 6341 6735 7142

Agriculture (F) 366 305 275 257 248 245 246 250 258 268 281 295 312 330 350 371


98 108 119 131 144 157 172 188 204 222 242 263 285 309 335 363

Industry (L) 64 64 64 63 63 63 63 63 62 62 62 62 62 61 61 61

PWW (S + M + L)

261 279 297 315 333 351 368 386 403 420 437 454 471 487 503 519

Mixed Load 22 26 31 35 39 44 48 52 57 61 65 70 74 78 82 87


3328 3572 3863 4176 4513 4867 5243 5633 6041 6465 6913 7372 7846 8335 8840 9361


13.1.20. Jalore

Table 137: Jalore - Forecast of electricity consumption using trend method


Domestic 134 144 154 165 175 187 200 211 221 229 237 243 243 237 227 212

Non-Domestic 55 62 70 79 88 97 106 116 127 139 154 172 191 212 235 260

Public Street Light

3 3 3 3 3 3 3 3 3 3 3 3 2 2 2 2


841 925 1017 1117 1226 1334 1451 1576 1710 1855 2009 2175 2351 2540 2740 2953

Agriculture (F) 352 340 328 316 304 301 298 295 291 288 284 280 275 271 266 261


179 191 203 216 230 245 260 277 295 313 333 354 376 400 425 451

Industry (L) 29 32 35 39 43 45 48 50 53 56 59 62 65 69 72 76

PWW (S + M + L)

55 59 62 66 69 70 70 70 70 69 69 68 67 66 65 64

Mixed Load 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3


1651 1758 1875 2002 2141 2285 2438 2601 2773 2955 3152 3359 3575 3800 4035 4282


Table 138: Jalore - Forecast of electricity consumption using HW method


Domestic 184 214 237 257 275 293 309 325 341 356 371 387 402 417 433 448

Non-Domestic 60 65 70 74 79 84 88 93 98 102 107 112 116 121 126 130

Public Street Light

3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3


1196 1303 1411 1519 1626 1734 1841 1949 2057 2164 2272 2379 2487 2594 2702 2810

Agriculture (F) 329 313 303 299 298 300 303 309 316 324 333 342 353 365 377 389


185 200 216 231 246 261 276 291 306 321 337 352 367 382 397 412

Industry (L) 37 38 38 39 40 41 42 43 44 44 45 46 47 48 49 50

PWW (S + M + L)

59 64 70 75 81 86 92 97 103 108 114 119 125 130 136 141

Mixed Load 3 4 4 5 5 6 7 7 8 8 9 9 10 11 11 12


2056 2204 2352 2502 2654 2807 2961 3117 3274 3431 3590 3750 3910 4071 4233 4396


Table 139: Jalore - Forecast of electricity consumption using econometric method


Domestic 130 140 151 163 175 187 200 214 228 242 257 272 286 298 309 319

Non-Domestic 56 64 73 82 93 102 111 121 131 142 154 166 180 194 209 230

Public Street Light

3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3


811 880 954 1033 1117 1195 1279 1367 1459 1557 1662 1772 1887 2010 2139 2296

Agriculture (F) 236 227 224 223 225 229 235 242 250 259 270 281 293 306 319 333


176 190 205 221 237 254 272 291 311 331 353 377 401 426 453 481

Industry (L) 36 37 38 39 40 41 42 42 43 44 45 46 47 48 48 49

PWW (S + M + L)

58 63 69 74 79 85 90 95 100 106 111 116 121 126 130 135

Mixed Load 3 4 4 5 5 6 7 7 8 8 9 9 10 10 11 12


1510 1609 1721 1842 1975 2102 2238 2382 2533 2693 2864 3042 3227 3420 3621 3858


13.1.21. Pali and Sirohi

Table 140: Pali and Sirohi - Forecast of electricity consumption using trend method


Domestic 462 502 543 586 628 659 688 712 731 743 763 773 767 744 706 660

Non-Domestic 158 174 192 211 232 251 271 292 314 338 371 406 444 486 530 587

Public Street Light

13 13 14 14 14 14 13 13 12 11 10 10 9 8 7 6


490 538 591 649 711 779 853 933 1020 1114 1215 1325 1442 1569 1704 1836

Agriculture (F) 39 36 34 31 29 27 25 23 21 20 18 17 15 14 13 13


139 145 150 155 161 166 172 178 185 191 198 204 211 219 226 240

Industry (L) 380 413 448 487 528 541 554 567 581 594 608 622 636 650 664 699

PWW (S + M + L)

50 52 53 55 57 56 56 55 54 53 52 51 49 48 46 45

Mixed Load 12 12 12 12 12 11 11 11 11 11 11 11 11 11 11 11


1742 1884 2036 2199 2373 2506 2643 2785 2929 3075 3246 3418 3585 3747 3907 4096


Table 141: Pali and Sirohi - Forecast of electricity consumption using HW method


Domestic 519 557 595 633 671 709 747 785 823 861 899 937 975 1013 1051 1089

Non-Domestic 196 213 230 247 264 281 298 314 331 348 365 382 399 416 433 450

Public Street Light

15 15 16 17 18 19 19 20 21 22 23 24 24 25 26 27


621 719 816 914 1012 1110 1208 1306 1404 1502 1599 1697 1795 1893 1991 2089

Agriculture (F) 56 54 52 50 48 46 44 42 40 38 36 34 32 30 28 26


140 144 148 151 155 159 163 167 171 175 179 182 186 190 194 198

Industry (L) 527 562 576 581 580 575 567 556 543 528 512 495 476 457 436 415

PWW (S + M + L)

50 52 54 56 58 59 61 63 65 67 69 70 72 74 76 78

Mixed Load 14 15 17 18 20 22 23 25 26 28 29 31 32 34 35 37


2137 2331 2504 2668 2826 2980 3130 3278 3424 3568 3711 3852 3993 4132 4271 4409


Table 142: Pali and Sirohi - Forecast of electricity consumption using econometric method


Domestic 455 490 527 564 601 640 678 717 756 795 832 869 899 923 943 959

Non-Domestic 159 182 208 235 265 284 305 326 348 371 395 421 447 475 504 553

Public Street Light

13 14 15 16 16 17 18 18 19 20 20 21 21 22 22 22


476 513 552 594 639 686 737 791 848 908 974 1043 1116 1193 1275 1363

Agriculture (F) 40 39 38 37 36 35 34 33 32 31 29 28 27 25 24 22


139 143 147 151 154 158 162 166 170 173 177 181 185 189 193 196

Industry (L) 524 559 573 578 577 572 563 552 540 524 508 491 473 454 433 412

PWW (S + M + L)

50 52 53 55 57 59 60 62 64 65 67 68 70 71 73 74

Mixed Load 14 15 17 18 20 21 23 24 26 27 29 30 32 33 35 36


1871 2008 2131 2249 2366 2472 2580 2690 2801 2915 3033 3152 3269 3385 3501 3639


13.1.22. Barmer and Jaisalmer

Table 143: Barmer and Jaisalmer - Forecast of electricity consumption using trend method


Domestic 271 305 341 381 422 449 475 499 519 535 551 559 555 539 513 479

Non-Domestic 132 146 161 178 197 216 237 259 284 310 345 384 426 473 524 580

Public Street Light

7 7 7 8 8 8 7 7 7 6 6 5 5 4 4 3


1369 1572 1804 2069 2371 2518 2673 2834 3003 3179 3362 3552 3750 3953 4164 4487

Agriculture (F) 39 34 31 27 24 16 11 7 5 3 2 2 1 1 0 0


71 74 77 80 83 84 84 85 85 86 86 87 87 87 88 93

Industry (L) 267 319 381 455 543 577 614 653 694 738 784 833 885 940 998 1050

PWW (S + M + L)

108 113 118 124 129 129 129 128 127 126 124 123 121 118 115 113

Mixed Load 80 83 86 90 94 97 101 105 109 113 117 121 125 129 133 134


2343 2653 3007 3411 3871 4095 4331 4578 4833 5096 5378 5665 5954 6245 6540 6941


Table 144: Barmer and Jaisalmer - Forecast of electricity consumption using HW method


Domestic 297 324 350 377 403 430 457 483 510 536 563 590 616 643 670 696

Non-Domestic 168 188 208 228 248 268 288 307 327 347 367 387 407 427 447 467

Public Street Light

11 12 12 13 14 15 16 17 18 19 19 20 21 22 23 24


1783 1943 2104 2264 2424 2585 2745 2905 3066 3226 3386 3547 3707 3867 4028 4188

Agriculture (F) 46 39 32 25 18 11 4 0 0 0 0 0 0 0 0 0


71 73 75 76 78 80 81 83 85 86 88 90 91 93 95 96

Industry (L) 162 170 178 185 193 201 209 216 224 232 240 248 255 263 271 279

PWW (S + M + L)

105 110 116 121 126 132 137 143 148 153 159 164 169 175 180 186

Mixed Load 76 79 81 83 85 87 90 92 94 96 98 101 103 105 107 109


2720 2937 3155 3373 3591 3809 4026 4247 4471 4696 4921 5146 5370 5595 5820 6044


Table 145: Barmer and Jaisalmer - Forecast of electricity consumption using econometric method


Domestic 272 310 354 403 457 519 587 663 747 841 943 1056 1172 1292 1418 1552

Non-Domestic 122 133 145 158 172 187 203 219 237 256 276 297 320 343 369 396

Public Street Light

11 11 12 13 14 15 15 16 17 18 18 19 19 20 20 20


1341 1482 1629 1781 1941 2105 2278 2454 2637 2827 3026 3230 3440 3658 3883 4116

Agriculture (F) 33 28 24 19 14 9 3 0 0 0 0 0 0 0 0 0


71 73 74 76 78 79 81 82 84 86 87 89 91 92 94 96

Industry (L) 273 331 397 471 556 652 761 885 1026 1183 1363 1565 1794 2052 2341 2669

PWW (S + M + L)

104 109 114 120 125 130 135 140 145 150 155 159 164 169 173 178

Mixed Load 76 78 80 82 84 86 88 91 93 95 97 99 101 103 105 107


2302 2555 2830 3123 3440 3781 4152 4551 4986 5454 5965 6514 7101 7729 8404 9133


13.1.23. Bikaner

Table 146: Bikaner - Forecast of electricity consumption using trend method


Domestic 378 415 453 493 534 570 604 636 664 687 709 721 718 700 668 624

Non-Domestic 129 141 155 171 187 204 223 242 264 287 318 352 389 429 474 524

Public Street Light

15 14 14 14 14 13 12 12 11 10 9 8 8 7 6 5


2564 2895 3266 3681 4147 4386 4634 4893 5162 5441 5729 6026 6333 6648 6972 7513

Agriculture (F) 182 165 150 137 124 112 102 92 84 76 68 62 55 50 45 44


129 134 139 145 150 150 150 149 149 148 148 147 147 146 146 155

Industry (L) 78 83 89 96 103 107 112 117 122 127 133 138 144 150 157 165

PWW (S + M + L)

139 145 151 157 163 167 172 176 180 184 187 190 192 194 195 191

Mixed Load 63 64 65 66 66 65 65 64 63 62 61 60 59 58 57 57


3676 4057 4483 4958 5487 5775 6074 6382 6699 7022 7362 7705 8046 8383 8718 9278


Table 147: Bikaner - Forecast of electricity consumption using HW method


Domestic 422 452 482 511 541 571 600 630 660 690 719 749 779 808 838 868

Non-Domestic 154 167 181 194 207 220 234 247 260 274 287 300 313 327 340 353

Public Street Light

24 25 26 26 27 28 28 29 30 30 31 32 32 33 34 34


3470 3729 3988 4247 4506 4765 5024 5282 5541 5800 6059 6318 6577 6836 7095 7354

Agriculture (F) 255 259 263 266 270 274 278 282 286 290 294 297 301 305 309 313


137 142 147 152 158 163 168 174 179 184 189 195 200 205 210 216

Industry (L) 79 83 88 93 98 102 107 112 116 121 126 131 135 140 145 149

PWW (S + M + L)

140 146 153 160 167 173 180 187 193 200 207 214 220 227 234 240

Mixed Load 65 67 69 71 73 75 77 79 81 83 85 87 89 91 93 95


4745 5070 5395 5721 6046 6371 6696 7021 7346 7671 7996 8322 8647 8972 9297 9622


Table 148: Bikaner - Forecast of electricity consumption using econometric method


Domestic 361 391 422 453 485 517 550 583 616 649 681 712 738 758 776 790

Non-Domestic 115 125 136 148 160 173 187 201 217 233 251 269 289 310 332 356

Public Street Light

24 25 25 26 26 27 27 28 28 29 29 29 29 29 29 29


2332 2664 3028 3419 3847 4306 4808 5345 5927 6555 7241 7975 8767 9622 10544 11538

Agriculture (F) 183 188 193 199 204 210 215 221 227 232 238 244 250 256 262 268


130 137 143 150 157 165 173 181 190 199 209 219 230 241 252 265

Industry (L) 77 82 88 95 101 108 115 123 131 139 148 157 167 177 188 199

PWW (S + M + L)

139 146 153 160 167 174 182 190 197 205 213 221 229 237 245 253

Mixed Load 64 66 68 70 72 74 76 77 79 81 83 85 87 89 91 93


3424 3823 4256 4718 5219 5753 6332 6949 7612 8322 9093 9912 10785 11719 12719 13791


13.1.24. Sri Ganganagar

Table 149: Sri Ganganagar - Forecast of electricity consumption using trend method


Domestic 521 563 605 648 691 727 760 789 811 826 840 843 827 794 746 697

Non-Domestic 124 137 151 166 183 201 219 239 261 284 315 350 387 428 473 524

Public Street Light

6 6 7 8 8 8 8 8 7 7 7 6 6 5 5 4


128 144 162 182 204 210 216 222 228 234 239 245 251 256 261 281

Agriculture (F) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


51 51 52 53 54 54 55 56 57 58 58 59 60 61 62 65

Industry (L) 109 114 119 124 129 133 137 140 144 148 152 156 160 164 168 177

PWW (S + M + L)

23 24 25 27 28 30 32 35 37 40 44 48 52 58 64 71

Mixed Load 74 74 75 75 76 76 76 77 77 77 77 77 77 77 77 77


1035 1113 1196 1283 1374 1439 1503 1565 1622 1674 1732 1783 1819 1842 1856 1896


Table 150: Sri Ganganagar - Forecast of electricity consumption using HW method


Domestic 590 635 680 724 769 814 858 903 948 992 1037 1082 1126 1171 1216 1261

Non-Domestic 147 161 176 191 206 221 236 251 265 280 295 310 325 340 355 369

Public Street Light

6 6 7 8 8 9 9 10 10 11 11 12 12 13 14 14


194 196 197 199 200 201 203 204 206 207 208 210 211 213 214 215

Agriculture (F) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


62 62 63 65 69 74 79 85 92 99 107 115 124 133 143 153

Industry (L) 114 131 145 157 168 177 186 195 203 210 0 0 0 0 0 0

PWW (S + M + L)

39 41 42 43 44 45 46 46 47 47 48 48 48 48 48 48

Mixed Load 78 80 81 83 85 86 88 90 91 93 94 96 98 99 101 103


1230 1312 1391 1470 1549 1627 1705 1783 1862 1940 1801 1873 1945 2017 2090 2163


Table 151: Sri Ganganagar - Forecast of electricity consumption using econometric method


Domestic 515 557 602 647 692 739 786 833 881 928 974 1020 1056 1086 1111 1133

Non-Domestic 130 147 166 186 207 225 245 265 287 310 334 360 387 416 446 487

Public Street Light

6 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


139 142 145 148 151 154 157 160 163 166 169 172 175 178 181 185

Agriculture (F) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


49 49 49 49 49 49 49 49 49 49 49 48 48 47 47 46

Industry (L) 114 131 144 156 167 176 185 193 201 209 0 0 0 0 0 0

PWW (S + M + L)

38 40 42 43 44 45 45 46 46 46 46 46 46 46 46 46

Mixed Load 77 79 81 82 84 85 87 88 90 91 93 94 96 97 99 100


1069 1146 1229 1311 1394 1474 1554 1635 1717 1799 1665 1740 1809 1871 1931 1996


13.1.25. Hanumangarh

Table 152: Hanumangarh - Forecast of electricity consumption using trend method


Domestic 394 424 454 485 515 550 584 615 642 664 685 697 694 677 646 603

Non-Domestic 74 81 88 96 105 116 127 141 155 170 191 214 239 267 299 331

Public Street Light

4 4 5 5 5 5 5 5 5 5 5 4 4 4 4 3


424 476 535 601 674 711 749 788 828 870 913 958 1003 1050 1097 1183

Agriculture (F) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


34 35 36 37 37 38 39 40 40 41 42 43 44 44 45 48

Industry (L) 39 39 40 41 42 42 43 44 45 46 46 47 48 49 50 52

PWW (S + M + L)

50 55 60 65 71 73 76 78 80 82 84 85 87 88 89 87

Mixed Load 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3


1022 1118 1221 1333 1452 1539 1626 1713 1799 1881 1969 2052 2123 2183 2232 2310


Table 153: Hanumangarh - Forecast of electricity consumption using HW method


Domestic 441 475 510 545 580 614 649 684 719 753 788 823 858 892 927 962

Non-Domestic 93 102 111 120 128 137 146 155 164 172 181 190 199 208 216 225

Public Street Light

4 4 5 5 5 5 5 6 6 6 6 6 7 7 7 7


603 624 646 667 689 710 732 753 774 796 817 839 860 882 903 924

Agriculture (F) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


33 33 33 33 32 32 32 32 32 32 32 32 31 31 31 31

Industry (L) 32 32 32 31 31 31 31 31 31 30 30 30 30 30 30 30

PWW (S + M + L)

45 39 37 37 37 38 39 41 42 44 47 49 52 54 57 60

Mixed Load 5 12 10 16 18 17 5 4 4 4 5 12 10 16 18 17


1256 1322 1383 1453 1520 1585 1640 1705 1771 1838 1907 1980 2047 2119 2189 2256


Table 154: Hanumangarh - Forecast of electricity consumption using econometric method


Domestic 397 432 468 504 541 580 618 657 696 735 772 810 840 865 886 905

Non-Domestic 77 88 100 113 127 142 158 175 194 214 235 258 283 309 338 368

Public Street Light

4 4 5 5 5 5 5 5 6 6 6 6 6 6 6 6


424 461 500 539 581 624 669 714 762 811 863 915 970 1026 1084 1144

Agriculture (F) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0


33 33 32 32 32 32 32 32 32 32 32 31 31 31 31 31

Industry (L) 32 32 31 31 31 31 31 31 30 30 30 30 30 30 29 29

PWW (S + M + L)

49 54 58 62 66 69 73 77 80 84 87 90 93 96 99 101

Mixed Load 5 11 10 16 18 17 5 4 4 4 5 11 10 16 18 17


1021 1114 1204 1302 1401 1499 1591 1695 1803 1914 2030 2152 2263 2379 2491 2600


13.1.26. Churu

Table 155: Churu - Forecast of electricity consumption using trend method


Domestic 313 337 362 386 411 434 456 475 491 503 514 518 511 493 465 434

Non-Domestic 74 82 90 99 109 120 132 145 159 174 195 217 242 270 300 332

Public Street Light

7 7 8 8 9 9 9 9 10 10 10 10 9 9 8 7


787 856 930 1010 1096 1129 1162 1195 1228 1261 1294 1326 1357 1388 1418 1528

Agriculture (F) 93 78 65 55 46 38 32 27 22 18 15 13 11 9 7 7


41 43 45 47 49 52 54 56 59 62 64 67 70 73 77 81

Industry (L) 28 31 33 36 40 41 42 43 44 45 47 48 49 50 52 54

PWW (S + M + L)

120 125 130 135 140 142 143 144 145 146 146 146 145 144 143 140

Mixed Load 6 6 6 5 5 5 5 5 5 5 5 5 5 5 5 5


1470 1565 1669 1782 1904 1969 2035 2100 2164 2224 2289 2349 2399 2441 2474 2589


Table 156: Churu - Forecast of electricity consumption using HW method


Domestic 347 369 392 415 438 460 483 506 528 551 574 597 619 642 665 688

Non-Domestic 88 93 98 103 108 113 117 122 127 132 137 142 147 152 157 162

Public Street Light

10 11 13 14 15 16 18 19 20 22 23 24 25 27 28 29


909 967 1024 1081 1138 1195 1253 1310 1367 1424 1481 1539 1596 1653 1710 1767

Agriculture (F) 59 49 41 34 27 22 17 12 8 4 0 -3 -6 -9 -12 -14


44 46 48 50 52 53 55 57 59 61 63 65 67 69 71 72

Industry (L) 31 35 39 44 48 53 57 62 66 70 75 79 84 88 93 97

PWW (S + M + L)

124 130 136 142 148 155 161 167 173 179 185 191 198 204 210 216

Mixed Load 8 8 9 10 11 12 13 14 14 15 16 17 18 19 20 20


1619 1708 1800 1892 1985 2079 2173 2268 2363 2459 2555 2651 2747 2844 2941 3038


Table 157: Churu - Forecast of electricity consumption using econometric method


Domestic 307 333 360 388 417 446 477 508 540 572 604 636 663 687 708 726

Non-Domestic 60 68 77 86 95 105 116 128 141 154 169 184 200 218 237 257

Public Street Light

10 11 12 14 15 16 17 18 19 20 21 22 23 24 24 25


807 892 984 1081 1185 1296 1414 1539 1672 1814 1966 2126 2297 2477 2670 2874

Agriculture (F) 42 36 30 25 21 17 13 9 6 3 0 -3 -5 -8 -10 -12


40 41 43 44 45 47 49 50 52 54 56 59 61 64 66 69

Industry (L) 30 35 39 44 48 52 57 61 66 70 74 79 83 88 92 96

PWW (S + M + L)

114 117 118 119 120 119 118 116 114 110 105 99 92 83 73 62

Mixed Load 8 8 9 10 11 12 13 13 14 15 16 17 17 18 19 20


1419 1542 1673 1810 1957 2111 2274 2445 2624 2812 3012 3219 3432 3651 3879 4116


13.2. Sample data formats

1. Format for capturing daily disruption in supply hours at circle/sub-division level. Data source: SE (Meters) or department that is responsible for Metering, Testing and Issuance of meters in Utilities

The data accessed for the present study for supply hours had either disruption counts or average disruption in a feeder per day. There was no mention of categories

served to, whether disruption was in peak or-non peak hours and the reason of failure was not specified. Hence, the same have been added to the suggested

format, so that complete data can be captured for analysis.

Existing format

Suggested format – data to be captured for each day

Circle Name Sub-division

name

Feeder name Date 1 Date 2 Date 3 Date 4 Date 5

Date Circle/ Sub-

division Name

Feeder name

Feeder level

Urban/ Rural

Consumer category to which it is

tagged

Feeder Switch

Off time

Feeder load before

interruption/ Last recorded hourly value

of load

Feeder Switch

On time

Feeder load after

restoration/ interruption

Supply disruptio

n (in minutes)

Peak

Supply disruption

(in minutes)

Non Peak

Reason of Failure. Data sheet should have options to select/ capture reasons

1. DT Failure 2. Feeder breakdown 3. Scheduled load

shedding 4. Capacity constraint 5. Others


2. Sales/ Connected load/ Number of consumers at Circle level/ Sub-division level to be captured for each month Data source: Respective Circles/ Commercial department

Data is captured in the given format presently. Existing format can be continued to capture data

Existing format

3. Load factor – capture at granular level wherever possible Data source: State SLDC

Load factor data pertaining to consumer categories across months are not available. The data accessibility should be provided in case such data was already

captured.

Suggested format to capture load factor

Consumer Categories

Urban/ Rural

Average Supply

duration (Hr)

April May June July August Sept Oct Nov Dec Jan Feb Mar

Domestic 0.71 0.75 0.69

Industry 0.30 0.32 0.33

Consumer Categories

Urban/ Rural

April May June July August Sept Oct Nov Dec Jan Feb Mar


4. Details of Open Access and Captive consumers (Load in MW, Electricity in MUs to be captured) Source: Commercial department of Utilities, Energy Department

Data for open access across months are maintained in given format. Data for captive power plants should also be captured on a monthly basis in given format.

Existing format for OA. To be used for Captive consumers also

5. Format for new Connections added per month for all categories – Domestic, Commercial, HT/ bulk consumer (Industries, SEZ,

Government establishments, Housing complexes etc.) Source: HT Billing, Metering department, Energy department

Data in suggested format should be made available across portals. Currently there is no specific format available.

Suggested format

Details of Open Access consumers

Feeder level

(33kV/ 11kV)

Load April May June July August Sept Oct Nov Dec Jan Feb Mar

M/s ABC

M/s DEF

Circle Sub-division Connection applied in

Month/ Year

Name of Consumer

Contract Demand

Supply Voltage

Expected Month/ Year

of load

Connection approved/ released (Yes/ No)


6. Monthly details of Agriculture connections released/ Solar pumps etc. installed Source: Commercial Department, Energy department

The data pertaining to number of connections are captured along with average load of pumps. Detailed list of connections in suggested format should be

captured for record of the utilities/ Government.

Suggested format

7. Monthly/ Quarterly data for Independent variables – Data at District/ Circle level Source: Government Planning Department, Economics and Statistics department

Yearly data of variables are available at a State level. In case it is captured on a monthly/ quarterly basis at a more granular level, it can be utilized for monthly

forecasts.

Suggested format

Independent variables State/

District April May June July Aug Sept Oct Nov Dec Jan Feb Mar

Population

Per capita income

Disposable income

Consumers

WPI/CPI

Wages

Circle Sub-division Agriculture Connection

issued

Name and details

Contract Demand

Supply Voltage

Capacity of pump (solar,

non-solar etc.)

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